Search Results for author: Craig Macdonald

Found 37 papers, 15 papers with code

Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering

no code implementations EMNLP (scai) 2020 Sarawoot Kongyoung, Craig Macdonald, Iadh Ounis

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.

Conversational Question Answering Conversational Search +1

Shallow Cross-Encoders for Low-Latency Retrieval

1 code implementation29 Mar 2024 Aleksandr V. Petrov, Sean MacAvaney, Craig Macdonald

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.

Passage Ranking Retrieval +1

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning

1 code implementation7 Mar 2024 Aleksandr Petrov, Craig Macdonald

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.

reinforcement-learning Re-Ranking +1

What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems

no code implementations11 Jan 2024 Maria Vlachou, Craig Macdonald

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).

Image Captioning Recommendation Systems

RecJPQ: Training Large-Catalogue Sequential Recommenders

no code implementations11 Dec 2023 Aleksandr V. Petrov, Craig Macdonald

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.

Passage Retrieval Retrieval +1

A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation

no code implementations27 Nov 2023 Siwei Liu, Xi Wang, Craig Macdonald, Iadh Ounis

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).

Recommendation Systems

Large Multi-modal Encoders for Recommendation

no code implementations31 Oct 2023 Zixuan Yi, Zijun Long, Iadh Ounis, Craig Macdonald, Richard McCreadie

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.

Recommendation Systems

On Coherence-based Predictors for Dense Query Performance Prediction

no code implementations17 Oct 2023 Maria Vlachou, Craig Macdonald

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.


Contrastive Graph Prompt-tuning for Cross-domain Recommendation

no code implementations21 Aug 2023 Zixuan Yi, Iadh Ounis, Craig Macdonald

In our research, we introduce the Personalised Graph Prompt-based Recommendation (PGPRec) framework.

Contrastive Learning Recommendation Systems

gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

2 code implementations14 Aug 2023 Aleksandr Petrov, Craig Macdonald

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.

Sequential Recommendation

Generative Query Reformulation for Effective Adhoc Search

no code implementations1 Aug 2023 Xiao Wang, Sean MacAvaney, Craig Macdonald, Iadh Ounis

GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information.

Information Retrieval Retrieval

Generative Sequential Recommendation with GPTRec

no code implementations19 Jun 2023 Aleksandr V. Petrov, Craig Macdonald

This paper presents the GPTRec sequential recommendation model, which is based on the GPT-2 architecture.

Sequential Recommendation

Doc2Query--: When Less is More

1 code implementation9 Jan 2023 Mitko Gospodinov, Sean MacAvaney, Craig Macdonald

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.

Hallucination Retrieval

Entity-Assisted Language Models for Identifying Check-worthy Sentences

no code implementations19 Nov 2022 Ting Su, Craig Macdonald, Iadh Ounis

Our results show that the neural language models significantly outperform traditional TF. IDF and LSTM methods.

Entity Embeddings Sentence +2

Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter

no code implementations19 Nov 2022 Ting Su, Craig Macdonald, Iadh Ounis

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.

Fake News Detection Graph Embedding +2

Adaptive Re-Ranking with a Corpus Graph

1 code implementation18 Aug 2022 Sean MacAvaney, Nicola Tonellotto, Craig Macdonald

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores.

Passage Ranking Re-Ranking +1

A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation

1 code implementation15 Jul 2022 Aleksandr Petrov, Craig Macdonald

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.

Sequential Recommendation

Effective and Efficient Training for Sequential Recommendation using Recency Sampling

1 code implementation6 Jul 2022 Aleksandr Petrov, Craig Macdonald

Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations.

Sequential Recommendation

Reproducing Personalised Session Search over the AOL Query Log

no code implementations21 Jan 2022 Sean MacAvaney, Craig Macdonald, Iadh Ounis

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).

Session Search

Streamlining Evaluation with ir-measures

no code implementations26 Nov 2021 Sean MacAvaney, Craig Macdonald, Iadh Ounis

We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval.

Information Retrieval Retrieval

On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval

1 code implementation25 Aug 2021 Craig Macdonald, Nicola Tonellotto

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.

Passage Ranking Retrieval

Query Embedding Pruning for Dense Retrieval

1 code implementation23 Aug 2021 Nicola Tonellotto, Craig Macdonald

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.

Passage Ranking Retrieval

On Single and Multiple Representations in Dense Passage Retrieval

1 code implementation13 Aug 2021 Craig Macdonald, Nicola Tonellotto, Iadh Ounis

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.

Passage Retrieval Re-Ranking +1

IntenT5: Search Result Diversification using Causal Language Models

no code implementations9 Aug 2021 Sean MacAvaney, Craig Macdonald, Roderick Murray-Smith, Iadh Ounis

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.

Causal Language Modeling Language Modelling +1

Graph Neural Pre-training for Enhancing Recommendations using Side Information

no code implementations8 Jul 2021 Zaiqiao Meng, Siwei Liu, Craig Macdonald, Iadh Ounis

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.

Entity Embeddings Recommendation Systems

Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval

3 code implementations21 Jun 2021 Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis

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.

Information Retrieval Passage Ranking +2

The Simpson's Paradox in the Offline Evaluation of Recommendation Systems

1 code implementation18 Apr 2021 Amir H. Jadidinejad, Craig Macdonald, Iadh Ounis

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!

Recommendation Systems

Leveraging Review Properties for Effective Recommendation

no code implementations5 Feb 2021 Xi Wang, Iadh Ounis, Craig Macdonald

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.

Recommendation Systems

Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification

no code implementations14 Aug 2020 Xi Wang, Iadh Ounis, Craig Macdonald

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.

Binary Classification Classification +1

Declarative Experimentation in Information Retrieval using PyTerrier

8 code implementations28 Jul 2020 Craig Macdonald, Nicola Tonellotto

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.

Information Retrieval Retrieval

Exploring Data Splitting Strategies for the Evaluation of Recommendation Models

no code implementations26 Jul 2020 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

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.

Recommendation Systems

Deep Reinforced Query Reformulation for Information Retrieval

no code implementations15 Jul 2020 Xiao Wang, Craig Macdonald, Iadh Ounis

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.

Document Ranking Information Retrieval +1

Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format

2 code implementations18 Mar 2020 Jimmy Lin, Joel Mackenzie, Chris Kamphuis, Craig Macdonald, Antonio Mallia, Michał Siedlaczek, Andrew Trotman, Arjen de Vries

There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems.

Variational Bayesian Context-aware Representation for Grocery Recommendation

1 code implementation17 Sep 2019 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

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.

Variational Inference

The FACTS of Technology-Assisted Sensitivity Review

no code implementations5 Jul 2019 Graham McDonald, Craig Macdonald, Iadh Ounis

However, many government documents contain sensitive information, such as personal or confidential information.


Using Word Embeddings in Twitter Election Classification

no code implementations22 Jun 2016 Xiao Yang, Craig Macdonald, Iadh Ounis

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.

Classification General Classification +3

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