no code implementations • 27 Jan 2025 • Jingtong Gao, Zhaocheng Du, Xiaopeng Li, Yichao Wang, Xiangyang Li, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
This limitation arises from their difficulty in capturing complex distributions and understanding feature relationships from sparse and limited data, along with their inability to grasp semantic feature relations.
1 code implementation • 23 Dec 2024 • Xiaopeng Li, Jingtong Gao, Pengyue Jia, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Wang, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention.
no code implementations • 22 Dec 2024 • Yewen Li, Shuai Mao, Jingtong Gao, Nan Jiang, Yunjian Xu, Qingpeng Cai, Fei Pan, Peng Jiang, Bo An
We use weak-to-strong search alignment by training small critics for different preferences and an MCTS-inspired search to refine the model's output.
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 • 18 Jun 2024 • Jingtong Gao, Bo Chen, Weiwen Liu, Xiangyang Li, Yichao Wang, Wanyu Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.
no code implementations • 6 Jun 2024 • Sheng Zhang, Maolin Wang, Wanyu Wang, Jingtong Gao, Xiangyu Zhao, Yu Yang, Xuetao Wei, Zitao Liu, Tong Xu
Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations.
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 • 5 Sep 2023 • Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang
To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.
2 code implementations • 8 Feb 2023 • Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
1 code implementation • 7 Feb 2023 • Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
no code implementations • 21 Sep 2022 • Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.