activity
20222024
most citedTowards Representation Alignment and Uniformity in Collaborative Filtering

200 citations · 351 across the 31 of their papers we have counts for

collaborators

31 papers

cs.CL20243 cited

Mitigating Entity-Level Hallucination in Large Language Models

Weihang Su, Yichen Tang, Qingyao Ai +3

The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions w…

cs.IR2024

STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals

Weihang Su, Yiran Hu, Anzhe Xie +6

Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated jud…

cs.IR2024

EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation

Shaorun Zhang, Zhiyu He, Ziyi Ye +4

In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems p…

cs.IR2024

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

Haitao Li, You Chen, Zhekai Ge +4

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims t…

cs.IR2024

DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment

Haitao Li, Qingyao Ai, Xinyan Han +5

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation abili…

cs.IR20243 cited

Sequential Recommendation with Latent Relations based on Large Language Model

Shenghao Yang, Weizhi Ma, Peijie Sun +4

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…