98 citations · 486 across the 56 of their papers we have counts for
14 papers · 1 filter
Group based Personalized Search by Integrating Search Behaviour and Friend Network
Yujia Zhou, Zhicheng Dou, Bingzheng Wei +1
The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were pro…
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Yujia Zhou, Zhicheng Dou, Yutao Zhu +1
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made g…
Towards More Effective and Economic Sparsely-Activated Model
Hao Jiang, Ke Zhan, Jianwei Qu +14
The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become…
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
Peitian Zhang, Zhicheng Dou, Jing Yao
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a…
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Jing Yao, Zhicheng Dou, Ruobing Xie +3
Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right t…
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
Zhengyi Ma, Zhicheng Dou, Yutao Zhu +2
Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. E…
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Zhicheng Dou +5
Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to…
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
Zhengyi Ma, Zhicheng Dou, Wei Xu +4
Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especiall…
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Hongjin Qian, Zhicheng Dou, Yutao Zhu +2
In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of…
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
Yutao Zhu, Jian-Yun Nie, Kun Zhou +3
A proactive dialogue system has the ability to proactively lead the conversation. Different from the general chatbots which only react to the user, proactive dialogue systems can b…
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Hao Jiang, Yutao Zhu, Xinyu Zhang +4
Recent years have witnessed great progress on building emotional chatbots. Tremendous methods have been proposed for chatbots to generate responses with given emotions. However, th…
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
Yuqi Huo, Manli Zhang, Guangzhen Liu +32
Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction bet…
Neural Sentence Ordering Based on Constraint Graphs
Yutao Zhu, Kun Zhou, Jian-Yun Nie +2
Sentence ordering aims at arranging a list of sentences in the correct order. Based on the observation that sentence order at different distances may rely on different types of inf…
Content Selection Network for Document-grounded Retrieval-based Chatbots
Yutao Zhu, Jian-Yun Nie, Kun Zhou +2
Grounding human-machine conversation in a document is an effective way to improve the performance of retrieval-based chatbots. However, only a part of the document content may be r…