Search Results for author: Beihong Jin

Found 11 papers, 1 papers with code

Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism

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).

Document Ranking Retrieval

A Deep Behavior Path Matching Network for Click-Through Rate Prediction

no code implementations1 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.

Click-Through Rate Prediction Contrastive Learning +1

A New Approach to Training Multiple Cooperative Agents for Autonomous Driving

no code implementations5 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.

Autonomous Driving Decision Making

Improving Micro-video Recommendation by Controlling Position Bias

no code implementations9 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.

Contrastive Learning Sequential Recommendation

Improving Micro-video Recommendation via Contrastive Multiple Interests

1 code implementation19 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.

Contrastive Learning

HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification

no code implementations18 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

TITA: A Two-stage Interaction and Topic-Aware Text Matching Model

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.

Text Matching Vocal Bursts Valence Prediction

Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

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.


Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

no code implementations10 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.

Sequential Recommendation

A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features

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.

text-classification Text Classification +1

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