no code implementations • EMNLP 2021 • Xingwu Sun, Yanling Cui, Hongyin Tang, Fuzheng Zhang, Beihong Jin, Shi Wang
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
no code implementations • 1 Feb 2023 • Jian Dong, Yisong Yu, Yapeng Zhang, Yimin Lv, Shuli Wang, Beihong Jin, Yongkang Wang, Xingxing Wang, Dong Wang
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making.
no code implementations • 5 Sep 2022 • Ruiyang Yang, Siheng Li, Beihong Jin
Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge.
no code implementations • 9 Aug 2022 • Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, Wei Zhu
Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario.
1 code implementation • 19 May 2022 • Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhuo
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.
no code implementations • 18 Apr 2022 • Pengfei Gao, Jingpeng Zhao, Yinglong Ma, Ahmad Tanvir, Beihong Jin
Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy.
Multi Label Text Classification Multi-Label Text Classification +1
no code implementations • 27 Jun 2021 • Wei Zhuo, Kunchi Liu, Taofeng Xue, Beihong Jin, Beibei Li, Xinzhou Dong, He Chen, Wenhai Pan, Xuejian Zhang, Shuo Zhou
Interactions between users and videos are the major data source of performing video recommendation.
no code implementations • NAACL 2021 • Xingwu Sun, Yanling Cui, Hongyin Tang, Qiuyu Zhu, Fuzheng Zhang, Beihong Jin
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
no code implementations • ACL 2021 • Hongyin Tang, Xingwu Sun, Beihong Jin, Jingang Wang, Fuzheng Zhang, Wei Wu
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.
no code implementations • 10 Mar 2021 • Xinzhou Dong, Beihong Jin, Wei Zhuo, Beibei Li, Taofeng Xue
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.
no code implementations • IJCNLP 2019 • Hongyin Tang, Miao Li, Beihong Jin
This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics.