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Wayne Xin Zhao — most-cited papers & profile · Recommender Systems
← authors
·
overview
Wayne Xin Zhao
71
papers ·
2375
citations ·
56
h-index
Renmin University of China
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
2020 · 302 citations
Towards Universal Sequence Representation Learning for Recommender Systems
2022 · 226 citations
Towards Topic-Guided Conversational Recommender System
2020 · 146 citations
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
2022 · 146 citations
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
2022 · 139 citations
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
2022 · 102 citations
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
2020 · 79 citations
Heterogeneous Information Network Embedding for Recommendation
2017 · 74 citations
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
2023 · 64 citations
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
2020 · 39 citations
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
2023 · 25 citations
Large Language Models are Zero-Shot Rankers for Recommender Systems
2023 · 23 citations
Alleviating the Long-Tail Problem in Conversational Recommender Systems
2023 · 16 citations
A Long-Short Demands-Aware Model for Next-Item Recommendation
2019 · 11 citations
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
2022 · 10 citations
Top co-authors
Ji-Rong Wen
· 21
Yupeng Hou
· 11
Xiaolei Wang
· 6
Ruobing Xie
· 5
Kun Zhou
· 4
Shanlei Mu
· 4
Yaliang Li
· 4
Julian McAuley
· 3
Junjie Zhang
· 3
Leyu Lin
· 3
Shuqing Bian
· 3
Wenqi Sun
· 3
Topics
Sequential & Session
LLM-based
Ranking & CTR
Collaborative Filtering
Cold Start & Cross-Domain
Two-Tower & Retrieval
Graph-based
Fairness & Ethics