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CTR-Guided Generative Query Suggestion in Conversational Search
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. W…
Leveraging LLMs to Evaluate Usefulness of Document
Xingzhu Wang, Erhan Zhang, Yiqun Chen +7
The conventional Cranfield paradigm struggles to effectively capture user satisfaction due to its weak correlation between relevance and satisfaction, alongside the high costs of r…
From Prompting to Alignment: A Generative Framework for Query Recommendation
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
In modern search systems, search engines often suggest relevant queries to users through various panels or components, helping refine their information needs. Traditionally, these…
Hgformer: Hyperbolic Graph Transformer for Recommendation
Xin Yang, Xingrun Li, Heng Chang +8
The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effectiv…
Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models
Wenhan Liu, Xinyu Ma, Yutao Zhu +4
Large Language Models (LLMs) have shown exciting performance in listwise passage ranking. Due to the limited input length, existing methods often adopt the sliding window strategy.…
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)
Yuchen Li, Haoyi Xiong, Linghe Kong +4
Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter…