Education

• 2014.09 - 2018.06:
  • B.S. in Zhejiang University

Work experience

• 2018.07 - Now:
  • Serving as Senior Algorithm Engineer
  • In EdgeRec team of Taobao Homepage Guess U Like, Taobao Technique Department, Alibaba Group
  • Promoting efficiency in re-ranking algorithms:
    • Proposed a context-wise generative re-ranking model GRN, contributing to 5.2% PV lift and 6.1% IPV lift
    • Proposed a permutation-wise re-ranking framework PRS, contributing to 11.0% PV lift and 8.7% IPV lift
    • Participated in the implementation of recommender system in the edge computing (EdgeRec)
  • Promoting user behavior modeling in CTR prediction:
    • Proposed a session-based CTR prediction model DSIN, contributing to 5.2% CTR lift
    • Proposed KG-enhanced CTR prediction models MTBRN and ATRBG, contributing to 6.8% CTR lift
    • Implemented a CTR prediction framework based on tensorflow, contributing to significant CTR lifts in various scenarios
  • Promoting related fields of the research and industry:
    • Published 4 papers in SIGIR, CIKM and IJCAI, three of which I am the first authorship
    • Submitted 2 papers to SIGIR-2021 and KDD-2021, both of which I am the first authorship

Publications

Deep session interest network for click-through rate prediction Permalink

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation Permalink

MTBRN: Multiplex Target-Behavior Relation EnhancedNetwork for Click-Through Rate Prediction Permalink

EdgeRec: Recommender System on Edge in Mobile Taobao Permalink

GRN: Generative Rerank Network for Context-wise Recommendation Permalink

Revisit Recommender System in the Permutation Prospective Permalink

Skills

• Recommender system
  • Matching, Ranking and Re-ranking
  • Data mining
• Deep learning, Tensorflow
• Python, Java and Sql

Research Interests

• Matching, Ranking and Re-ranking algorithms in Recommender system
• NLP (Natural Language Processing)
• IR (Information Retrieval)
• RL (Reinforcement Learning)