1. A Scalable Approach for Periodical Personalized Recommendations
2. Adaptive, Personalized Diversity for Visual Discovery
3. Field-aware Factorization Machines for CTR Prediction
4. Local Item-Item Models for Top-N Recommendation （Best paper）
5. Mechanism Design for Personalized Recommender Systems
6. Deep Neural Networks for YouTube Recommendations
7. Past, Present, and Future of Recommender Systems: An Industry Perspective （author：Xavier Amatriain）
8. Algorithms Aside: Recommendation as the Lens Of Life （演讲的胶片非常艺术流）
9. Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
10. Are You Influenced by Others When Rating? Improve Rating Prediction by Conformity Modeling （余勇老师组做的工作）

11. Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Task
工业界的几篇论文：
1. When Recommendation Systems Go Bad (meetup)
2. News Recommendations at scale at Bloomberg Media: Challenges and Approaches (Bloomber)
3. Marsbot: Building a Personal Assistant (Foursqure)
4. Music Personalization at Spotify (Spotify)
5. Recommending for the World (Netflix)
6. The Exploit-Explore Dilemma in Music Recommendation (Pandora)
7. Tutorial: Lessons Learned from Building Real-life Recommender Systems (Xavier’ tutorial)