108 citations · 421 across the 36 of their papers we have counts for
37 papers
PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID
Xiao Wang, Sean MacAvaney, Craig Macdonald
Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Metho…
All Eyes on the Ranker: Participatory Auditing to Surface Blind Spots in Ranked Search Results
Anna Marie Rezk, Patrizia Di Campli San Vito, Ayah Soufan +3
Search engines that present users with a ranked list of search results are a fundamental technology for providing public access to information. Evaluations of such systems are typi…
Shallow Cross-Encoders for Low-Latency Retrieval
Aleksandr V. Petrov, Sean MacAvaney, Craig Macdonald
Transformer-based Cross-Encoders achieve state-of-the-art effectiveness in text retrieval. However, Cross-Encoders based on large transformer models (such as BERT or T5) are comput…
A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation
Siwei Liu, Xi Wang, Craig Macdonald +1
The use of pre-training is an emerging technique to enhance a neural model's performance, which has been shown to be effective for many neural language models such as BERT. This te…
Large Multi-modal Encoders for Recommendation
Zixuan Yi, Zijun Long, Iadh Ounis +2
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 e…
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
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 scor…
Contrastive Graph Prompt-tuning for Cross-domain Recommendation
Zixuan Yi, Iadh Ounis, Craig Macdonald
Recommender systems are frequently challenged by the data sparsity problem. One approach to mitigate this issue is through cross-domain recommendation techniques. In a cross-domain…
Generative Query Reformulation for Effective Adhoc Search
Xiao Wang, Sean MacAvaney, Craig Macdonald +1
Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance…
Generative Sequential Recommendation with GPTRec
Aleksandr V. Petrov, Craig Macdonald
Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-b…
Doc2Query--: When Less is More
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-s…
Entity-Assisted Language Models for Identifying Check-worthy Sentences
Ting Su, Craig Macdonald, Iadh Ounis
We propose a new uniform framework for text classification and ranking that can automate the process of identifying check-worthy sentences in political debates and speech transcrip…
Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter
Ting Su, Craig Macdonald, Iadh Ounis
Social networks (SNs) are increasingly important sources of news for many people. The online connections made by users allows information to spread more easily than traditional new…
Adaptive Re-Ranking with a Corpus Graph
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. The process enables the use o…
A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation
Aleksandr Petrov, Craig Macdonald
BERT4Rec is an effective model for sequential recommendation based on the Transformer architecture. In the original publication, BERT4Rec claimed superiority over other available s…
Effective and Efficient Training for Sequential Recommendation using Recency Sampling
Aleksandr Petrov, Craig Macdonald
Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increas…
Reproducing Personalised Session Search over the AOL Query Log
Sean MacAvaney, Craig Macdonald, Iadh Ounis
Despite its troubled past, the AOL Query Log continues to be an important resource to the research community -- particularly for tasks like search personalisation. When using the q…
Streamlining Evaluation with ir-measures
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. Rather than implementing its own measur…
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
Craig Macdonald, Nicola Tonellotto
Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) te…
Query Embedding Pruning for Dense Retrieval
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 us…
On Single and Multiple Representations in Dense Passage Retrieval
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,…
IntenT5: Search Result Diversification using Causal Language Models
Sean MacAvaney, Craig Macdonald, Roderick Murray-Smith +1
Search result diversification is a beneficial approach to overcome under-specified queries, such as those that are ambiguous or multi-faceted. Existing approaches often rely on mas…
Graph Neural Pre-training for Enhancing Recommendations using Side Information
Zaiqiao Meng, Siwei Liu, Craig Macdonald +1
Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important mode…
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
Xiao Wang, Craig Macdonald, Nicola Tonellotto +1
Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users' initial queries using information occu…
The Simpson's Paradox in the Offline Evaluation of Recommendation Systems
Amir H. Jadidinejad, Craig Macdonald, Iadh Ounis
Recommendation systems are often evaluated based on user's interactions that were collected from an existing, already deployed recommendation system. In this situation, users only…