Netflix at RecSys 2016 – Recap
By [Justin Basilico](https://twitter.com/JustinBasilico) and [Yves Raimond](https://twitter.com/moustaki)
A key aspect of Netflix is providing our members with a personalized experience so they can easily find great stories to enjoy. A collection of recommender systems drive the main aspects of this personalized experience and we continuously work on researching and testing new ways to make them better. As such, we were delighted to sponsor and participate in this year’s [ACM Conference on Recommender Systems](https://recsys.acm.org/recsys16/) in Boston, which marked the 10th anniversary of the conference. For those who couldn’t attend or want more information, here is a recap of our talks and papers at the conference.
Justin and Yves gave a talk titled “[Recommending for the World](http://www.slideshare.net/justinbasilico/recommending-for-the-world-66144921)” on how we prepared our algorithms to work world-wide ahead of our global launch earlier this year. You can also read more about it in our previous [blog](http://techblog.netflix.com/2016/02/recommending-for-world.html) [posts](https://media.netflix.com/en/company-blog/a-global-approach-to-recommendations).
**[Recommending for the World](https://www.slideshare.net/justinbasilico/recommending-for-the-world-66144921 "Recommending for the World")**
Justin also teamed up with [Xavier Amatriain](http://xavier.amatriain.net/), formerly at Netflix and now at Quora, in the special Past, Present, and Future track to offer an [industry perspective](http://www.slideshare.net/justinbasilico/past-present-future-of-recommender-systems-an-industry-perspective) on what the future of recommender systems in industry may be.
**[Past, Present & Future of Recommender Systems: An Industry Perspective](https://www.slideshare.net/justinbasilico/past-present-future-of-recommender-systems-an-industry-perspective "Past, Present & Future of Recommender Systems: An Industry Perspective")**
Chao-Yuan Wu [presented](http://www.andrew.cmu.edu/user/chaoyuaw/RecSys2016_slides.pdf) a [paper](http://dl.acm.org/citation.cfm?id=2959174) he authored last year while at Netflix, on how to use navigation information to adapt recommendations within a session as you learn more about user intent.
[](http://1.bp.blogspot.com/-9awzKSsgbss/WBPogZkmROI/AAAAAAAABRs/vAVhBg48Y5INO4R_i3XS_PyRaRPdUjb8gCK4B/s1600/Screen%2BShot%2B2016-10-28%2Bat%2B5.07.56%2BPM.png)
**[Using Navigation to Improve Recommendations in Real-Time](http://www.andrew.cmu.edu/user/chaoyuaw/RecSys2016_slides.pdf "Past, Present & Future of Recommender Systems: An Industry Perspective")**
Yves also shared some [pitfalls of distributed learning](http://www.slideshare.net/moustaki/some-pitfalls-of-distributed-learning) at the Large Scale Recommender Systems workshop.
**[(Some) pitfalls of distributed learning](https://www.slideshare.net/moustaki/some-pitfalls-of-distributed-learning "(Some) pitfalls of distributed learning")**
Hossein Taghavi gave a [presentation](http://www.slideshare.net/MohammadHosseinTagha/balancing-discovery-and-continuation-in-recommendations/1) at the RecSysTV workshop on trying to balance discovery and continuation in recommendations, which is also the subject of a recent [blog post](http://techblog.netflix.com/2016/10/to-be-continued.html).
**[Balancing Discovery and Continuation in Recommendations](https://www.slideshare.net/MohammadHosseinTagha/balancing-discovery-and-continuation-in-recommendations "Balancing Discovery and Continuation in Recommendations")**
Dawen Liang [presented](http://www.slideshare.net/cheerz/factorization-meets-the-item-embedding-regularizing-matrix-factorization-with-item-cooccurrence) some [research](http://dl.acm.org/citation.cfm?id=2959182) he conducted prior to joining Netflix on combining matrix factorization and item embedding.
**[Factorization Meets the Item Embedding:](https://www.slideshare.net/cheerz/factorization-meets-the-item-embedding-regularizing-matrix-factorization-with-item-cooccurrence "Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence")**
**[Regularizing Matrix Factorization with Item Co-occurrence](https://www.slideshare.net/cheerz/factorization-meets-the-item-embedding-regularizing-matrix-factorization-with-item-cooccurrence "Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence")**