108 citations · 352 across the 26 of their papers we have counts for
25 papers · 1 filter
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…
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…