1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
1 code implementation • 28 Feb 2024 • Lanling Xu, Zhen Tian, Bingqian Li, Junjie Zhang, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao
The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.
no code implementations • 10 Jan 2024 • Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao, Ji-Rong Wen
As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs.
1 code implementation • 2 Nov 2023 • Yifan Du, Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Jinpeng Wang, Chuyuan Wang, Mingchen Cai, Ruihua Song, Ji-Rong Wen
By conducting a comprehensive empirical study, we find that instructions focused on complex visual reasoning tasks are particularly effective in improving the performance of MLLMs on evaluation benchmarks.
1 code implementation • 23 Aug 2023 • Chenrui Zhang, Lin Liu, Jinpeng Wang, Chuyuan Wang, Xiao Sun, Hongyu Wang, Mingchen Cai
Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting.
no code implementations • 17 Apr 2023 • Xiao Sun, Bo Zhang, Chenrui Zhang, Han Ren, Mingchen Cai
AUC is a common metric for evaluating the performance of a classifier.
no code implementations • 6 Apr 2021 • Qiang Cui, Chenrui Zhang, Yafeng Zhang, Jinpeng Wang, Mingchen Cai
Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest.