1.6k citations · 6.6k across the 108 of their papers we have counts for
11 papers · 1 filter
Towards Topic-Guided Conversational Recommender System
Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao +2
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality da…
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou +16
In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, th…
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Wayne Xin Zhao, Junhua Chen, Pengfei Wang +2
Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriat…
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
Junyi Li, Siqing Li, Wayne Xin Zhao +4
Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous…
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Shuqing Bian, Xu Chen, Wayne Xin Zhao +5
With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typical…
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Kun Zhou, Wayne Xin Zhao, Hui Wang +4
Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference repre…
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao +5
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction l…
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
Kun Zhou, Wayne Xin Zhao, Shuqing Bian +3
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major…
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Ruiyang Ren, Zhaoyang Liu, Yaliang Li +4
Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained wit…
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Gaole He, Junyi Li, Wayne Xin Zhao +2
The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to…
Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting
Kun Zhou, Wayne Xin Zhao, Yutao Zhu +2
Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters. However, in practice, the negative samples of training data a…