Search Results for author:
Found 37 papers, 15 papers with code
Declarative Experimentation in Information Retrieval using PyTerrier
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures.
Effective and Efficient Training for Sequential Recommendation using Recency Sampling
Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations.
A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation
We also propose our own implementation of BERT4Rec based on the Hugging Face Transformers library, which we demonstrate replicates the originally reported results on 3 out 4 datasets, while requiring up to 95% less training time to converge.
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, we extract representative feedback embeddings (using KMeans clustering) -- while ensuring that these embeddings discriminate among passages (based on IDF) -- which are then added to the query representation.
Ranked #1 on
TREC 2019 Passage Ranking
on MSMARCO
On Single and Multiple Representations in Dense Passage Retrieval
The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense retrieval.
Query Embedding Pruning for Dense Retrieval
Recent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to use such models to identify documents from the collection in the first place.
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored.
Doc2Query--: When Less is More
Doc2Query -- the process of expanding the content of a document before indexing using a sequence-to-sequence model -- has emerged as a prominent technique for improving the first-stage retrieval effectiveness of search engines.
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling.
Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format
There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems.
Adaptive Re-Ranking with a Corpus Graph
Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores.
Variational Bayesian Context-aware Representation for Grocery Recommendation
We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour.
Shallow Cross-Encoders for Low-Latency Retrieval
However, Cross-Encoders based on large transformer models (such as BERT or T5) are computationally expensive and allow for scoring only a small number of documents within a reasonably small latency window.
The Simpson's Paradox in the Offline Evaluation of Recommendation Systems
Using the relative comparison of many recommendation models as in the typical offline evaluation of recommender systems, and based on the Kendall rank correlation coefficient, we show that our proposed evaluation methodology exhibits statistically significant improvements of 14% and 40% on the examined open loop datasets (Yahoo!
Using Word Embeddings in Twitter Election Classification
In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance.
The FACTS of Technology-Assisted Sensitivity Review
However, many government documents contain sensitive information, such as personal or confidential information.
Deep Reinforced Query Reformulation for Information Retrieval
Query reformulations have long been a key mechanism to alleviate the vocabulary-mismatch problem in information retrieval, for example by expanding the queries with related query terms or by generating paraphrases of the queries.
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems.
Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification
However, a classification model that learns to classify binary instances with incomplete positive labels while assuming all unlabelled data to be negative examples will often generate a biased classifier.
Leveraging Review Properties for Effective Recommendation
Furthermore, inspired by the users' information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users' preferences.
Graph Neural Pre-training for Enhancing Recommendations using Side Information
For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks.
IntenT5: Search Result Diversification using Causal Language Models
Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents.
Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering
Furthermore, we propose a novel hybrid dynamic method combining Abridged Linear for the main task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary tasks, so as to automatically fine-tune task weighting during learning, ensuring that each of the task’s weights is adjusted by the relative importance of the different tasks.
Streamlining Evaluation with ir-measures
We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval.
Reproducing Personalised Session Search over the AOL Query Log
Given that web documents are prone to change over time, we study the differences present between a version of the corpus containing documents as they appeared in 2017 (which has been used by several recent works) and a new version we construct that includes documents close to as they appeared at the time the query log was produced (2006).
Entity-Assisted Language Models for Identifying Check-worthy Sentences
Our results show that the neural language models significantly outperform traditional TF. IDF and LSTM methods.
Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter
We conclude that the Twitter users' friendship and followers network information can significantly outperform language-based approaches, as well as the existing state-of-the-art fake news detection models that use a more sophisticated network structure, in classifying fake news on Twitter.
Generative Sequential Recommendation with GPTRec
This paper presents the GPTRec sequential recommendation model, which is based on the GPT-2 architecture.
Generative Query Reformulation for Effective Adhoc Search
GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information.
Contrastive Graph Prompt-tuning for Cross-domain Recommendation
In our research, we introduce the Personalised Graph Prompt-based Recommendation (PGPRec) framework.
On Coherence-based Predictors for Dense Query Performance Prediction
Our approach introduces a new setting for obtaining richer information on query differences in dense QPP that can explain potential unstable performance of existing predictors and outlines the unique characteristics of different query types on dense retrieval models.
Large Multi-modal Encoders for Recommendation
In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can encode diverse content about each item.
A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation
We propose a novel recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data at the pre-training stage in a Graph Neural Network (GNN).
RecJPQ: Training Large-Catalogue Sequential Recommenders
Sequential Recommendation is a popular recommendation task that uses the order of user-item interaction to model evolving users' interests and sequential patterns in their behaviour.
What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems
Still, evaluating systems in offline settings with simulators suffers from problems, such as focusing entirely on a single target item (not addressing the exploratory nature of a recommender system), and exhibiting extreme patience (consistent feedback over a large number of turns).
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG.
