1 code implementation • 3 Nov 2024 • Langming Liu, Xiangyu Zhao, Chi Zhang, Jingtong Gao, Wanyu Wang, Wenqi Fan, Yiqi Wang, Ming He, Zitao Liu, Qing Li
Transformer models have achieved remarkable success in sequential recommender systems (SRSs).
no code implementations • 28 Oct 2024 • Chuang Zhao, Xing Su, Ming He, Hongke Zhao, Jianping Fan, Xiaomeng Li
Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions.
no code implementations • 14 Oct 2024 • Chenglei Shen, Jiahao Zhao, Xiao Zhang, Weijie Yu, Ming He, Jianping Fan
To address this issue, we propose a novel controllable learning approach via Parameter Diffusion for controllable multi-task Recommendation (PaDiRec), which allows the customization and adaptation of recommendation model parameters to new task requirements without retraining.
no code implementations • 10 Sep 2024 • Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Xiao Zhang, Ming He, Jianping Fan, Jun Xu
Specifically, MoRE introduces three reflectors for generating LLM-based reflections on explicit preferences, implicit preferences, and collaborative signals.
no code implementations • 19 Aug 2024 • Xinyu Li, Chuang Zhao, Hongke Zhao, Likang Wu, Ming He
In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of recommendation systems.
no code implementations • 4 Jul 2024 • Jiayi Zhang, Chuang Zhao, Yihan Zhao, Zhaoyang Yu, Ming He, Jianping Fan
The attainment of autonomous operations in mobile computing devices has consistently been a goal of human pursuit.
no code implementations • 25 Jun 2024 • Zhichen Xiang, Hongke Zhao, Chuang Zhao, Ming He, Jianping Fan
Data bias, e. g., popularity impairs the dynamics of two-sided markets within recommender systems.
1 code implementation • 24 Jun 2024 • Chuang Zhao, Hongke Zhao, Ming He, Xiaomeng Li, Jianping Fan
Furthermore, in addition to the supervised loss for overlapping users, we design contrastive tasks for non-overlapping users from both group and individual-levels to avoid model skew and enhance the semantics of representations.
no code implementations • 19 Jun 2024 • Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, Nan Feng
Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision and audio data.
no code implementations • 19 Jun 2024 • Zhong Guan, Hongke Zhao, Likang Wu, Ming He, Jianpin Fan
Specifically, since natural language descriptions are not sufficient for LLMs to understand and process graph-structured data, fine-tuned LLMs perform even worse than some traditional GNN models on graph tasks, lacking inherent modeling capabilities for graph structures.
no code implementations • 19 Jun 2024 • Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianpin Fan
To address this, we consider enhancing the learning capability of language model-driven recommendation models for structured data, specifically by utilizing interaction graphs rich in collaborative semantics.
no code implementations • 27 Feb 2024 • Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi Wang, Ming He, Zitao Liu, Hongzhi Yin
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
no code implementations • 26 Jan 2023 • Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, Jianping Fan
Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains.