Search Results for author: Maarten de Rijke

Found 170 papers, 108 papers with code

SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments

1 code implementation28 Nov 2023 Romain Deffayet, Thibaut Thonet, Dongyoon Hwang, Vassilissa Lehoux, Jean-Michel Renders, Maarten de Rijke

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.

counterfactual Learning-To-Rank +1

Learning Robust Sequential Recommenders through Confident Soft Labels

1 code implementation4 Nov 2023 Shiguang Wu, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Maarten de Rijke, Zhaochun Ren

CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.

Multi-class Classification Sequential Recommendation

Hierarchical Forecasting at Scale

1 code implementation19 Oct 2023 Olivier Sprangers, Wander Wadman, Sebastian Schelter, Maarten de Rijke

We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level.

Time Series

From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

1 code implementation18 Oct 2023 Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.

Fact Verification Retrieval

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features

1 code implementation15 Oct 2023 Zihan Wang, Ziqi Zhao, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren

To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.

Data Augmentation few-shot-ner +5

Predictive Uncertainty-based Bias Mitigation in Ranking

1 code implementation18 Sep 2023 Maria Heuss, Daniel Cohen, Masoud Mansoury, Maarten de Rijke, Carsten Eickhoff

Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined.


Continual Learning for Generative Retrieval over Dynamic Corpora

no code implementations29 Aug 2023 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents.

Continual Learning Quantization +1

Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method

no code implementations19 Aug 2023 Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query.

Adversarial Attack Contrastive Learning +1

Group Membership Bias

no code implementations5 Aug 2023 Ali Vardasbi, Maarten de Rijke, Fernando Diaz, Mostafa Dehghani

With group bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair.

Fairness Learning-To-Rank +1

Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping

1 code implementation2 Aug 2023 Ming Li, Mozhdeh Ariannezhad, Andrew Yates, Maarten de Rijke

In next basket recommendation (NBR), it is useful to distinguish between repeat items, i. e., items that a user has consumed before, and explore items, i. e., items that a user has not consumed before.

Next-basket recommendation

Answering Ambiguous Questions via Iterative Prompting

1 code implementation8 Jul 2023 Weiwei Sun, Hengyi Cai, Hongshen Chen, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.

Open-Domain Question Answering valid

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression

no code implementations26 May 2023 Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke

Successful applications of distributional reinforcement learning with quantile regression prompt a natural question: can we use other statistics to represent the distribution of returns?

Continuous Control Distributional Reinforcement Learning +2

MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering

1 code implementation22 May 2023 Vaishali Pal, Andrew Yates, Evangelos Kanoulas, Maarten de Rijke

Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table.

Question Answering

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

1 code implementation9 May 2023 Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon Jose, Maarten de Rijke, Zhaochun Ren

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.

Denoising Open-Ended Question Answering +2

On the Impact of Outlier Bias on User Clicks

1 code implementation1 May 2023 Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke

We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).

counterfactual Learning-To-Rank

Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models

1 code implementation28 Apr 2023 Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

In this paper, we focus on a more general type of perturbation and introduce the topic-oriented adversarial ranking attack task against NRMs, which aims to find an imperceptible perturbation that can promote a target document in ranking for a group of queries with the same topic.

Information Retrieval Retrieval

A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

1 code implementation28 Apr 2023 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, Xueqi Cheng

Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.


Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization

1 code implementation26 Apr 2023 Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke

For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.

counterfactual Learning-To-Rank

An Offline Metric for the Debiasedness of Click Models

2 code implementations19 Apr 2023 Romain Deffayet, Philipp Hager, Jean-Michel Renders, Maarten de Rijke

We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.

counterfactual Learning-To-Rank +1

Generative Slate Recommendation with Reinforcement Learning

no code implementations20 Jan 2023 Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, Maarten de Rijke

Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.

Recommendation Systems reinforcement-learning +2

Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study

1 code implementation12 Jan 2023 Mariya Hendriksen, Svitlana Vakulenko, Ernst Kuiper, Maarten de Rijke

Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets.

Cross-Modal Retrieval Retrieval

Modeling Sequential Recommendation as Missing Information Imputation

1 code implementation4 Jan 2023 Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren

To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.

Imputation Sequential Recommendation

Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives

no code implementations3 Jan 2023 Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, Maarten de Rijke

In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recommenders.

Offline RL Recommendation Systems +2

Contrastive Learning Reduces Hallucination in Conversations

1 code implementation20 Dec 2022 Weiwei Sun, Zhengliang Shi, Shen Gao, Pengjie Ren, Maarten de Rijke, Zhaochun Ren

MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.

Contrastive Learning

Feature-Level Debiased Natural Language Understanding

1 code implementation11 Dec 2022 Yougang Lyu, Piji Li, Yechang Yang, Maarten de Rijke, Pengjie Ren, Yukun Zhao, Dawei Yin, Zhaochun Ren

We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples.

Contrastive Learning Natural Language Understanding

RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations

no code implementations17 Sep 2022 Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, Maarten de Rijke

In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models.

Recommendation Systems

Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

1 code implementation14 Sep 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng

A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.

Information Retrieval Retrieval

Intersection of Parallels as an Early Stopping Criterion

1 code implementation19 Aug 2022 Ali Vardasbi, Maarten de Rijke, Mostafa Dehghani

Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting.

counterfactual Learning-To-Rank

Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users

no code implementations6 Jul 2022 Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski

In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.

Explainable Artificial Intelligence (XAI)

Debiasing Learning for Membership Inference Attacks Against Recommender Systems

1 code implementation24 Jun 2022 Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren

To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.

Recommendation Systems

A Simulation Environment and Reinforcement Learning Method for Waste Reduction

no code implementations30 May 2022 Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke

We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable.

Distributional Reinforcement Learning reinforcement-learning +1

Fairness of Exposure in Light of Incomplete Exposure Estimation

1 code implementation25 May 2022 Maria Heuss, Fatemeh Sarvi, Maarten de Rijke

In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution.


State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study

1 code implementation10 May 2022 Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke

In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset.

reinforcement-learning Reinforcement Learning (RL)

Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval

1 code implementation28 Apr 2022 Maurits Bleeker, Andrew Yates, Maarten de Rijke

We add an additional decoder to the contrastive ICR framework, to reconstruct the input caption in a latent space of a general-purpose sentence encoder, which prevents the image and caption encoder from suppressing predictive features.

Contrastive Learning Retrieval

Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking

1 code implementation28 Apr 2022 Ali Vardasbi, Fatemeh Sarvi, Maarten de Rijke

Different from PL, where pointwise logits are used as the distribution parameters, in PPG pairwise inversion probabilities together with a reference permutation construct the distribution.

Fairness Learning-To-Rank

Understanding User Satisfaction with Task-oriented Dialogue Systems

no code implementations26 Apr 2022 Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke

What is the influence of user experience on the user satisfaction rating of TDS as opposed to, or in addition to, utility?

Task-Oriented Dialogue Systems

Summarization with Graphical Elements

1 code implementation15 Apr 2022 Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke

Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people.

Text Summarization

Parameter-Efficient Abstractive Question Answering over Tables or Text

1 code implementation dialdoc (ACL) 2022 Vaishali Pal, Evangelos Kanoulas, Maarten de Rijke

In this work, we study parameter-efficient abstractive QA in encoder-decoder models over structured tabular data and unstructured textual data using only 1. 5% additional parameters for each modality.

abstractive question answering Question Answering

PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

no code implementations4 Apr 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list.

Document Ranking Information Retrieval +1

Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

2 code implementations2 Apr 2022 Weiwei Sun, Shuyu Guo, Shuo Zhang, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities.

Task-Oriented Dialogue Systems

Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?

1 code implementation14 Feb 2022 Maurits Bleeker, Maarten de Rijke

Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning.

Image Retrieval Metric Learning +2

Understanding and Mitigating the Effect of Outliers in Fair Ranking

1 code implementation21 Dec 2021 Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke

We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers.

Fairness Outlier Detection

Parameter Efficient Deep Probabilistic Forecasting

1 code implementation6 Dec 2021 Olivier Sprangers, Sebastian Schelter, Maarten de Rijke

However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.

Probabilistic Time Series Forecasting Time Series

It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic

1 code implementation24 Nov 2021 Jin Huang, Harrie Oosterhuis, Maarten de Rijke

We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased.

Recommendation Systems Selection bias

Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence

no code implementations1 Nov 2021 Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke

In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility.


A Next Basket Recommendation Reality Check

1 code implementation29 Sep 2021 Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten de Rijke

We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models.

Next-basket recommendation

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues

1 code implementation1 Sep 2021 Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen

(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.

Benchmarking Contrastive Learning +2

sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification

1 code implementation24 Aug 2021 Gabriel Bénédict, Vincent Koops, Daan Odijk, Maarten de Rijke

We propose a loss function, sigmoidF1, which is an approximation of the F1 score that (1) is smooth and tractable for stochastic gradient descent, (2) naturally approximates a multilabel metric, and (3) estimates label propensities and label counts.


A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues

1 code implementation ACL 2021 Yangjun Zhang, Pengjie Ren, Maarten de Rijke

HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation.

Dialogue Evaluation

News Article Retrieval in Context for Event-centric Narrative Creation

1 code implementation In2Writing (ACL) 2022 Nikos Voskarides, Edgar Meij, Sabrina Sauer, Maarten de Rijke

Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative.


Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

1 code implementation ACL 2021 Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e. g., anaphora and ellipsis.

Few-Shot Electronic Health Record Coding through Graph Contrastive Learning

1 code implementation29 Jun 2021 Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Evangelos Kanoulas, Maarten de Rijke

We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.

Contrastive Learning Few-Shot Learning

Improving Transformer-based Sequential Recommenders through Preference Editing

no code implementations23 Jun 2021 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, Maarten de Rijke

By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation.

Self-Supervised Learning Sequential Recommendation

Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression

1 code implementation3 Jun 2021 Olivier Sprangers, Sebastian Schelter, Maarten de Rijke

We propose Probabilistic Gradient Boosting Machines (PGBM), a method to create probabilistic predictions with a single ensemble of decision trees in a computationally efficient manner.

regression Time Series Analysis

Wizard of Search Engine: Access to Information Through Conversations with Search Engines

1 code implementation18 May 2021 Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zhumin Chen, Zhaochun Ren, Maarten de Rijke

(2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.

Intent Detection Keyphrase Extraction +1

Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems

1 code implementation30 Apr 2021 Ziming Li, Julia Kiseleva, Maarten de Rijke

The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.

Response Generation

A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles

1 code implementation16 Feb 2021 Jiahuan Pei, Pengjie Ren, Maarten de Rijke

We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3. 06% in terms of response selection accuracy compared to state-of-the-art methods.

Task-Oriented Dialogue Systems

Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank

1 code implementation11 Feb 2021 Harrie Oosterhuis, Maarten de Rijke

We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so.

counterfactual Learning-To-Rank +1

CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

1 code implementation5 Feb 2021 Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri

In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.


Abstractive Opinion Tagging

1 code implementation18 Jan 2021 Qintong Li, Piji Li, Xinyi Li, Zhaochun Ren, Zhumin Chen, Maarten de Rijke

In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews.

Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

no code implementations16 Dec 2020 Mariya Hendriksen, Ernst Kuiper, Pim Nauts, Sebastian Schelter, Maarten de Rijke

In this paper, we focus on purchase prediction for both anonymous and identified sessions on an e-commerce platform.


What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research

no code implementations NAACL 2022 Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke

Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.

Text Summarization

Unifying Online and Counterfactual Learning to Rank

1 code implementation8 Dec 2020 Harrie Oosterhuis, Maarten de Rijke

With the introduction of the intervention-aware estimator, we aim to bridge the online/counterfactual LTR division as it is shown to be highly effective in both online and counterfactual scenarios.

counterfactual Learning-To-Rank +1

Conversational Browsing

no code implementations7 Dec 2020 Svitlana Vakulenko, Vadim Savenkov, Maarten de Rijke

How can we better understand the mechanisms behind multi-turn information seeking dialogues?

Information Retrieval Question Answering +1

Mixed Information Flow for Cross-domain Sequential Recommendations

1 code implementation1 Dec 2020 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Jun Ma, Maarten de Rijke

One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.

Sequential Recommendation Transfer Learning

Star Graph Neural Networks for Session-based Recommendation

no code implementations CIKM 2020 Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, Maarten de Rijke

The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session.

Session-Based Recommendations

When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank

1 code implementation24 Aug 2020 Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke

Our main contribution is a new estimator based on affine corrections: it both reweights clicks and penalizes items displayed on ranks with high trust bias.

counterfactual Learning-To-Rank

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

1 code implementation24 Jul 2020 Harrie Oosterhuis, Maarten de Rijke

LogOpt turns the counterfactual approach - which is indifferent to the logging policy - into an online approach, where the algorithm decides what rankings to display.

counterfactual Selection bias

A Comparison of Supervised Learning to Match Methods for Product Search

1 code implementation20 Jul 2020 Fatemeh Sarvi, Nikos Voskarides, Lois Mooiman, Sebastian Schelter, Maarten de Rijke

As recent learning to match methods have made important advances in bridging the vocabulary gap for these traditional IR areas, we investigate their potential in the context of product search.

Information Retrieval Retrieval +1

Pre-Trained Models for Heterogeneous Information Networks

no code implementations7 Jul 2020 Yang Fang, Xiang Zhao, Yifan Chen, Weidong Xiao, Maarten de Rijke

We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network.

Clustering Link Prediction +3

Optimizing Interactive Systems via Data-Driven Objectives

no code implementations19 Jun 2020 Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke, Ryen W. White

Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.

Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank

no code implementations25 May 2020 Ali Vardasbi, Maarten de Rijke, Ilya Markov

Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS.

counterfactual Learning-To-Rank

An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues

no code implementations25 May 2020 Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system.

Conversational Search

Query Resolution for Conversational Search with Limited Supervision

1 code implementation24 May 2020 Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, Maarten de Rijke

Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution.

Conversational Search Passage Retrieval +1

Accelerated Convergence for Counterfactual Learning to Rank

1 code implementation21 May 2020 Rolf Jagerman, Maarten de Rijke

Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system.

counterfactual Learning-To-Rank

Policy-Aware Unbiased Learning to Rank for Top-k Rankings

1 code implementation18 May 2020 Harrie Oosterhuis, Maarten de Rijke

We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top-k ranking.

counterfactual Learning-To-Rank +1

Rethinking Item Importance in Session-based Recommendation

no code implementations9 May 2020 Zhiqiang Pan, Fei Cai, Yanxiang Ling, Maarten de Rijke

We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference.

Session-Based Recommendations

WN-Salience: A Corpus of News Articles with Entity Salience Annotations

no code implementations LREC 2020 Chuan Wu, Evangelos Kanoulas, Maarten de Rijke, Wei Lu

To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking.

Entity Linking

Conversations with Search Engines: SERP-based Conversational Response Generation

1 code implementation29 Apr 2020 Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, Maarten de Rijke

In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.

Conversational Response Generation Conversational Search +1

Guided Dialog Policy Learning without Adversarial Learning in the Loop

1 code implementation7 Apr 2020 Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao

Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.

Reinforcement Learning (RL)

TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation

1 code implementation26 Mar 2020 Shaojie Jiang, Thomas Wolf, Christof Monz, Maarten de Rijke

We hypothesize that the deeper reason is that in the training corpora, there are hard tokens that are more difficult for a generative model to learn than others and, once learning has finished, hard tokens are still under-learned, so that repetitive generations are more likely to happen.

Text Generation

Safe Exploration for Optimizing Contextual Bandits

1 code implementation2 Feb 2020 Rolf Jagerman, Ilya Markov, Maarten de Rijke

Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.

counterfactual Information Retrieval +7

Conversations with Documents. An Exploration of Document-Centered Assistance

no code implementations27 Jan 2020 Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, Ryen W. White

Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.

Bidirectional Scene Text Recognition with a Single Decoder

1 code implementation8 Dec 2019 Maurits Bleeker, Maarten de Rijke

We introduce the bidirectional Scene Text Transformer (Bi-STET), a novel bidirectional STR method with a single decoder for bidirectional text decoding.

Scene Text Recognition

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

no code implementations1 Dec 2019 Chang Li, Haoyun Feng, Maarten de Rijke

Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics.

Learning-To-Rank Recommendation Systems

FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles

1 code implementation27 Nov 2019 Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke

Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.


Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

2 code implementations19 Nov 2019 Jiahuan Pei, Pengjie Ren, Christof Monz, Maarten de Rijke

We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions.

Response Generation Task-Oriented Dialogue Systems

Understanding Multi-Head Attention in Abstractive Summarization

no code implementations10 Nov 2019 Joris Baan, Maartje ter Hoeve, Marlies van der Wees, Anne Schuth, Maarten de Rijke

Finally, we find that relative positions heads seem integral to summarization performance and persistently remain after pruning.

Abstractive Text Summarization Machine Translation +1

How to Not Measure Disentanglement

no code implementations12 Oct 2019 Anna Sepliarskaia, Julia Kiseleva, Maarten de Rijke

We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition.

Disentanglement Transfer Learning +1

Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations

no code implementations6 Oct 2019 Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).

Sequential Recommendation

Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification

no code implementations26 Sep 2019 Jianming Zheng, Fei Cai, Honghui Chen, Maarten de Rijke

We introduce the concept of interaction and propose a two-perspective interaction representation, that encapsulates a local and a global interaction representation.

General Classification text-classification +1

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

1 code implementation27 Aug 2019 Wanyu Chen, Pengjie Ren, Fei Cai, Maarten de Rijke

Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training.

Sequential Recommendation

Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation

1 code implementation26 Aug 2019 Pengjie Ren, Zhumin Chen, Christof Monz, Jun Ma, Maarten de Rijke

Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp.

Improving Outfit Recommendation with Co-supervision of Fashion Generation

no code implementations24 Aug 2019 Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.

Message Passing for Complex Question Answering over Knowledge Graphs

1 code implementation19 Aug 2019 Svitlana Vakulenko, Javier David Fernandez Garcia, Axel Polleres, Maarten de Rijke, Michael Cochez

We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers.

Knowledge Graphs Question Answering

RefNet: A Reference-aware Network for Background Based Conversation

1 code implementation18 Aug 2019 Chuan Meng, Pengjie Ren, Zhumin Chen, Christof Monz, Jun Ma, Maarten de Rijke

In this paper, we propose a Reference-aware Network (RefNet) to address the two issues.

Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting

1 code implementation17 Jul 2019 Ana Lucic, Hinda Haned, Maarten de Rijke

Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, both based on Monte Carlo simulations.

Unbiased Learning to Rank: Counterfactual and Online Approaches

no code implementations16 Jul 2019 Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke

Through randomization the effect of different types of bias can be removed from the learning process.

counterfactual Learning-To-Rank

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

2 code implementations15 Jul 2019 Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke

At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.

Benchmarking counterfactual +2

Proceedings of FACTS-IR 2019

no code implementations12 Jul 2019 Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand

The proceedings list for the program of FACTS-IR 2019, the Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval held at SIGIR 2019.

Fairness Information Retrieval +1

A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts

1 code implementation10 Jul 2019 Jiahuan Pei, Pengjie Ren, Maarten de Rijke

We propose a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few expert bots are combined to generate the response for a given dialogue context.

Task-Oriented Dialogue Systems

Joint Neural Collaborative Filtering for Recommender Systems

3 code implementations8 Jul 2019 Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke

Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix.

Collaborative Filtering Recommendation Systems

Explaining Predictions from Tree-based Boosting Ensembles

no code implementations4 Jul 2019 Ana Lucic, Hinda Haned, Maarten de Rijke

Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community.

counterfactual Counterfactual Explanation

Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?

no code implementations1 Jul 2019 Joris Baan, Maartje ter Hoeve, Marlies van der Wees, Anne Schuth, Maarten de Rijke

We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization.

Abstractive Text Summarization valid

SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection

no code implementations16 Jun 2019 Jiahuan Pei, Arent Stienstra, Julia Kiseleva, Maarten de Rijke

Obtaining key information from a complex, long dialogue context is challenging, especially when different sources of information are available, e. g., the user's utterances, the system's responses, and results retrieved from a knowledge base (KB).

Task-Oriented Dialogue Systems

Improving Background Based Conversation with Context-aware Knowledge Pre-selection

1 code implementation16 Jun 2019 Yangjun Zhang, Pengjie Ren, Maarten de Rijke

The latter generate responses thatare natural but not necessarily effective in leveraging background knowledge.

Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model

no code implementations29 May 2019 Chang Li, Maarten de Rijke

We consider the problem of identifying the K most attractive items and propose cascading non-stationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item.


ViTOR: Learning to Rank Webpages Based on Visual Features

no code implementations7 Mar 2019 Bram van den Akker, Ilya Markov, Maarten de Rijke

The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR).

General Classification Image Classification +2

Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss

2 code implementations25 Feb 2019 Shaojie Jiang, Pengjie Ren, Christof Monz, Maarten de Rijke

Specifically, we first analyze the influence of the commonly used Cross-Entropy (CE) loss function, and find that the CE loss function prefers high-frequency tokens, which results in low-diversity responses.

Response Generation

QRFA: A Data-Driven Model of Information-Seeking Dialogues

no code implementations27 Dec 2018 Svitlana Vakulenko, Kate Revoredo, Claudio Di Ciccio, Maarten de Rijke

Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems.

MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation

1 code implementation11 Dec 2018 Chang Li, Ilya Markov, Maarten de Rijke, Masrour Zoghi

Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.

Information Retrieval Online Ranker Evaluation +2

Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

1 code implementation9 Dec 2018 Ziming Li, Julia Kiseleva, Maarten de Rijke

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.

Dialogue Generation Imitation Learning +2

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

1 code implementation6 Dec 2018 Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, Maarten de Rijke

RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.

Session-Based Recommendations

HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents

1 code implementation12 Oct 2018 Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter, Maarten Marx, Jaap Kamps, Maarten de Rijke

For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.

Topic Models

Differentiable Unbiased Online Learning to Rank

1 code implementation22 Sep 2018 Harrie Oosterhuis, Maarten de Rijke

Instead, its gradient is based on inferring preferences between document pairs from user clicks and can optimize any differentiable model.


Incremental Sparse Bayesian Ordinal Regression

1 code implementation18 Jun 2018 Chang Li, Maarten de Rijke

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning.

Multi-Label Learning regression

Measuring Semantic Coherence of a Conversation

2 code implementations17 Jun 2018 Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, Axel Polleres

Conversational systems have become increasingly popular as a way for humans to interact with computers.

Knowledge Graphs

BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback

no code implementations15 Jun 2018 Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, Masrour Zoghi

In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.

Learning-To-Rank Re-Ranking +1

Open Domain Suggestion Mining: Problem Definition and Datasets

no code implementations6 Jun 2018 Sapna Negi, Maarten de Rijke, Paul Buitelaar

We first present an annotation study, and based on our observations propose a formal task definition and annotation procedure for creating benchmark datasets for suggestion mining.

Suggestion mining

Ranking for Relevance and Display Preferences in Complex Presentation Layouts

1 code implementation7 May 2018 Harrie Oosterhuis, Maarten de Rijke

Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand.


Finding Influential Training Samples for Gradient Boosted Decision Trees

1 code implementation ICML 2018 Boris Sharchilev, Yury Ustinovsky, Pavel Serdyukov, Maarten de Rijke

We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e. g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT).

Learning Data-Driven Objectives to Optimize Interactive Systems

no code implementations17 Feb 2018 Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke

Effective optimization is essential for interactive systems to provide a satisfactory user experience.

Deep Learning with Logged Bandit Feedback

no code implementations ICLR 2018 Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training.

counterfactual Object Recognition +1

Attentive Memory Networks: Efficient Machine Reading for Conversational Search

1 code implementation19 Dec 2017 Tom Kenter, Maarten de Rijke

We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions.

Conversational Search Information Retrieval +2

Conversational Exploratory Search via Interactive Storytelling

no code implementations15 Sep 2017 Svitlana Vakulenko, Ilya Markov, Maarten de Rijke

In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface.

Conversational Search Navigate +1

Neural Vector Spaces for Unsupervised Information Retrieval

4 code implementations9 Aug 2017 Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas

We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.

Document Ranking Feature Engineering +4

Structural Regularities in Text-based Entity Vector Spaces

1 code implementation25 Jul 2017 Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas

We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank.

Clustering Entity Retrieval +2

Neural Networks for Information Retrieval

no code implementations13 Jul 2017 Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.

Information Retrieval Retrieval

Semantic Entity Retrieval Toolkit

1 code implementation12 Jun 2017 Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas

Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention.

Clustering Entity Retrieval +2

The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams

1 code implementation15 Jan 2017 David Graus, Daan Odijk, Maarten de Rijke

We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, can be viewed as an online place for collective memory.

Pyndri: a Python Interface to the Indri Search Engine

1 code implementation3 Jan 2017 Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke

We introduce pyndri, a Python interface to the Indri search engine.

Large-scale Validation of Counterfactual Learning Methods: A Test-Bed

no code implementations1 Dec 2016 Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news.

counterfactual Off-policy evaluation +2

Getting Started with Neural Models for Semantic Matching in Web Search

no code implementations8 Nov 2016 Kezban Dilek Onal, Ismail Sengor Altingovde, Pinar Karagoz, Maarten de Rijke

We detail the required background and terminology, a taxonomy grouping the rapidly growing body of work in the area, and then survey work on neural models for semantic matching in the context of three tasks: query suggestion, ad retrieval, and document retrieval.

Information Retrieval Retrieval

Probabilistic Feature Selection and Classification Vector Machine

no code implementations18 Sep 2016 Bingbing Jiang, Chang Li, Maarten de Rijke, Xin Yao, Huanhuan Chen

The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.

Classification feature selection +1

Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model

1 code implementation2 Sep 2016 Aleksandr Chuklin, Maarten de Rijke

In this paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone.


Learning Latent Vector Spaces for Product Search

2 code implementations25 Aug 2016 Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.


Lexical Query Modeling in Session Search

1 code implementation23 Aug 2016 Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke

Lexical query modeling has been the leading paradigm for session search.

Session Search

Unsupervised, Efficient and Semantic Expertise Retrieval

1 code implementation23 Aug 2016 Christophe Van Gysel, Maarten de Rijke, Marcel Worring

We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches.

Feature Engineering Retrieval

Siamese CBOW: Optimizing Word Embeddings for Sentence Representations

2 code implementations ACL 2016 Tom Kenter, Alexey Borisov, Maarten de Rijke

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings.

Sentence Embeddings Word Embeddings

Computing Web-scale Topic Models using an Asynchronous Parameter Server

1 code implementation24 May 2016 Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.

Information Retrieval Retrieval +1