Search Results for author: Zhongxiang Sun

Found 9 papers, 6 papers with code

Large Language Models Enhanced Collaborative Filtering

no code implementations26 Mar 2024 Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang song, Xiao Zhang, Jun Xu

In this paper, drawing inspiration from the in-context learning and chain of thought reasoning in LLMs, we propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distils the world knowledge and reasoning capabilities of LLMs into collaborative filtering.

Collaborative Filtering In-Context Learning +2

Logic Rules as Explanations for Legal Case Retrieval

1 code implementation3 Mar 2024 Zhongxiang Sun, Kepu Zhang, Weijie Yu, Haoyu Wang, Jun Xu

In this paper, we address the issue of using logic rules to explain the results from legal case retrieval.

Retrieval

Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

no code implementations23 Sep 2023 Zihua Si, Zhongxiang Sun, Jiale Chen, Guozhang Chen, Xiaoxue Zang, Kai Zheng, Yang song, Xiao Zhang, Jun Xu

To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning.

Contrastive Learning Recommendation Systems +1

KuaiSAR: A Unified Search And Recommendation Dataset

no code implementations13 Jun 2023 Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Dewei Leng, Yanan Niu, Yang song, Xiao Zhang, Jun Xu

We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.

Multi-Task Learning Recommendation Systems

When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation

1 code implementation18 May 2023 Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang song, Kun Gai, Ji-Rong Wen

In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.

Contrastive Learning Disentanglement +1

Uncovering ChatGPT's Capabilities in Recommender Systems

1 code implementation3 May 2023 Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, Jun Xu

The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.

Explainable Recommendation Information Retrieval +2

A Short Survey of Viewing Large Language Models in Legal Aspect

1 code implementation16 Mar 2023 Zhongxiang Sun

In this survey, we explore the integration of LLMs into the field of law.

Law Article-Enhanced Legal Case Matching: a Causal Learning Approach

1 code implementation20 Oct 2022 Zhongxiang Sun, Jun Xu, Xiao Zhang, Zhenhua Dong, Ji-Rong Wen

We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.

Semantic Text Matching Text Matching

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