108 citations · 421 across the 36 of their papers we have counts for
9 papers · 1 filter
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…
Leveraging Review Properties for Effective Recommendation
Xi Wang, Iadh Ounis, Craig Macdonald
Many state-of-the-art recommendation systems leverage explicit item reviews posted by users by considering their usefulness in representing the users' preferences and describing th…