先荐导读：深入学习任何一门学科，都离不开对前沿知识的了解。对于推荐系统学习者来说，一年一度的RecSys大会就是了解学术界与工业界研究热点的最佳平台。鉴于此，在这篇文章中，我们把过往的RecSys论文整理成一个清单，列出了大家学习推荐系统必看的10篇RecSys论文。
下边这5篇是根据ACM数字图书馆中的阅读量整理出来的。在已发表的925篇论文中，这五篇论文是阅读量最高的。这五篇论文约占所有RecSys会议论文引用的12％！
· Performance of recommender algorithms on top-n recommendation tasks — 2010, by Paolo Cremonesi, Yehuda Koren, Roberto Turrin
· Trust-aware recommender systems — 2007, by Paolo Massa, Paolo Avesani
· A matrix factorization technique with trust propagation for recommendation in social networks — 2010, by Mohsen Jamali, Martin Ester
· Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering — 2010, by Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, Nuria Oliver
· Hidden factors and hidden topics: understanding rating dimensions with review text — 2013, by Julian McAuley, Jure Leskovec
自从2009年以来，每一年的ACM RecSys大会还会为当年作出较大贡献的论文进行颁奖，接下来的5篇论文在近5年内被评为了“最佳论文”。
· Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations — 2017, by X. Zhang, J. Zhao, J.C.S. Lui
· Local Item-Item Models for Top-N Recommendation — 2016, by E. Christakopoulou and G. Karypis
· Context-Aware Event Recommendation in Event-based Social Networks— 2015, by A. Macedo, L. Marinho and R. Santos
· Beyond Clicks: Dwell Time for Personalization — 2014, by X. Yi, L. Hong, E. Zhong, N. Nan Liu and S. Rajan
· A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems— 2013, by Y. Zhuang, W. Chin, Y. Juan and C. Lin (Best Paper)
获取更多详细资料，或申请产品试用，欢迎访问第四范式智能推荐产品先荐官网！本账号为第四范式智能推荐产品先荐的官方账号。本账号立足于计算机领域，特别是人工智能相关的前沿研究，旨在把更多与人工智能相关的知识分享给公众，从专业的角度促进公众对人工智能的理解；同时也希望为人工智能相关人员提供一个讨论、交流、学习的开放平台，从而早日让每个人都享受到人工智能创造的价值。
