Results 31 to 37 of about 1,749 (37)

Sequential Recommendation with Latent Relations based on Large Language Model

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items ...
Ai, Qingyao, Cai, Mingchen, Liu, Yiqun, Ma, Weizhi, Sun, Peijie, Yang, Shenghao, Zhang, Min   +6 more
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HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses

In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs). Recent approaches suffer from insufficient and repetitive knowledge retrieval,
Chu, Xu, Ding, Hongxin, Fang, Yue, Jiang, Xinke, Qiu, Rihong, Tang, Jinyi, Wang, Yasha, Wang, Zhiyuan, Xu, Yongxin, Zhang, Ruizhe, Zhao, Junfeng   +10 more
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ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation

Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to.
Chen, Bo, Chen, Jizheng, Du, Kounianhua, Lin, Jianghao, Tang, Ruiming, Zhang, Weinan   +5 more
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Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation

Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, controlling the distribution of recommended
He, Zhankui, Jha, Rahul, Kallus, Nathan, Liang, Dawen, McAuley, Julian, Steck, Harald, Xie, Zhouhang   +6 more
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Async Learned User Embeddings for Ads Delivery Optimization

In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance.
Fawaz, Labib, Li, Boyang, Li, Dai, Li, Hong, Li, Li, Liu, Chengye, Liu, Linfeng, Liu, Meng, Reddy, Sri, Tang, Mingwei, Wang, Hongnan, Wang, Xiangyu, Wei, Chenglin, Xie, Yuelei, Xu, Rengan, Xu, Yifan, Yang, Junjie, Zhang, Zehua, Zhu, Bill   +18 more
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Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous.
Kennington, Casey, Zhuang, Jun
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DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation

Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space.
Chen, Bo, Chen, Jizheng, Dai, Xinyi, Du, Kounianhua, Lin, Jianghao, Tang, Ruiming, Wang, Hangyu, Xi, Yunjia, Zhang, Weinan   +8 more
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