no code implementations • 31 Mar 2024 • Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
Recommender systems aim to predict user interest based on historical behavioral data.
no code implementations • 13 Oct 2023 • Jianghao Lin, Bo Chen, Hangyu Wang, Yunjia Xi, Yanru Qu, Xinyi Dai, Kangning Zhang, Ruiming Tang, Yong Yu, Weinan Zhang
Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals among features.
1 code implementation • 3 Aug 2023 • Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang
The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances.
1 code implementation • 9 Jun 2023 • Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang
In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
1 code implementation • 17 Nov 2022 • Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu
Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.
no code implementations • 11 Oct 2022 • Zhengbang Zhu, Rongjun Qin, JunJie Huang, Xinyi Dai, Yang Yu, Yong Yu, Weinan Zhang
The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption.
1 code implementation • 21 Jul 2022 • Khalid Oublal, Xinyi Dai
Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters.
1 code implementation • Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021 • Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Shuai Li, Ruiming Tang, Xiuqiang He, Jianye Hao, Yong Yu
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
1 code implementation • 20 Apr 2022 • Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu
MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.
1 code implementation • 19 Nov 2021 • Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
no code implementations • 18 Oct 2021 • Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.
1 code implementation • 13 Apr 2021 • Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.
no code implementations • 1 Nov 2020 • Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu
To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.
no code implementations • 18 Jun 2020 • Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
1 code implementation • Neurocomputing 2019 • Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu
Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods.
no code implementations • 14 Nov 2018 • Haokun Chen, Xinyi Dai, Han Cai, Wei-Nan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.