7 papers · 1 filter
Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders
Bohao Wang, Jiawei Chen, Feng Liu +5
Large language models (LLMs), owing to their extensive open-domain knowledge and semantic reasoning capabilities, have been increasingly integrated into recommender systems (RS). H…
Breaking the Top- Barrier: Advancing Top- Ranking Metrics Optimization in Recommender Systems
Weiqin Yang, Jiawei Chen, Shengjia Zhang +5
In the realm of recommender systems (RS), Top- ranking metrics such as NDCG@ are the gold standard for evaluating recommendation performance. However, during the training of…
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li +5
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective…
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Bohao Wang, Feng Liu, Jiawei Chen +7
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundament…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen +5
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this…