187 citations · 209 across the 6 of their papers we have counts for
6 papers
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
Haoyu Wang, Sunhao Dai, Haiyuan Zhao +6
Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their sem…
Inference Computation Scaling for Feature Augmentation in Recommendation Systems
Weihao Liu, Zhaocheng Du, Haiyuan Zhao +5
Large language models have become a powerful method for feature augmentation in recommendation systems. However, existing approaches relying on quick inference often suffer from in…
Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
Haiyuan Zhao, Guohao Cai, Jieming Zhu +3
In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from du…
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
Haiyuan Zhao, Lei Zhang, Jun Xu +3
In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but als…
Uncovering ChatGPT's Capabilities in Recommender Systems
Sunhao Dai, Ninglu Shao, Haiyuan Zhao +6
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows signi…
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
Haiyuan Zhao, Jun Xu, Xiao Zhang +3
Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data. In real applicatio…