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hubojing / All-For-Recommendation

What You Want Is What We Want to Recommend

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recommender-systemrecommendation-systemrecommendationrecomendation-algorithm

All-For-Recommendation

What You Want Is What We Want to Recommend

Conference会议

Classic Paper经典论文

  • Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
    PDF

  • Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198. PDF

  • Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet computing, 2003, 7(1): 76-80. PDF

  • Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37. PDF

  • Rendle S. Factorization machines[C]//2010 IEEE International Conference on Data Mining. IEEE, 2010: 995-1000. PDF

Frontier Paper前沿论文

由于篇幅过大，转移至 https://github.com/hubojing/All-For-Recommendation/blob/main/FrontierPaper.md

综述

  • A Survey on Session-based Recommender Systems
  • A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
  • A Recommendation Systems for Tourism Based on Social Networks: A Survey
  • Research Commentary on Recommendations with Side Information: A Survey and Research Directions
  • A Survey on Conversational Recommender Systems
  • Explainable Recommendation: A Survey and New Perspectives
  • Sequential Recommender Systems: Challenges, Progress and Prospects
  • A Survey on Knowledge Graph-Based Recommender Systems
  • Deep Learning on Knowledge Graph for Recommender System: A Survey
  • A survey on group recommender systems

Datasets数据集

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What You Want Is What We Want to Recommend

recommender-systemrecommendation-systemrecommendationrecomendation-algorithm