no code implementations • 26 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.
no code implementations • 15 Dec 2023 • Weicong Qin, Zelin Cao, Weijie Yu, Zihua Si, Sirui Chen, Jun Xu
Legal case retrieval and judgment prediction are crucial components in intelligent legal systems.
no code implementations • 23 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.
no code implementations • 13 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.
1 code implementation • 18 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.
1 code implementation • 3 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.
1 code implementation • 9 Feb 2022 • Zihua Si, Xueran Han, Xiao Zhang, Jun Xu, Yue Yin, Yang song, Ji-Rong Wen
In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results.