Recommendations and Results Organization in Netflix Search
Sudarshan Lamkhede, Christoph Kofler

TL;DR
This paper discusses enhancing Netflix's search results by integrating recommendation techniques and organizing search outputs to better satisfy diverse user intents and improve user experience.
Contribution
It introduces methods to incorporate recommendation systems into search results and organize them effectively, bridging the gap between browsing and searching experiences.
Findings
Improved search result relevance through recommendation integration
Enhanced user satisfaction by organizing search results effectively
Demonstrated benefits of combining recommendation and search techniques
Abstract
Personalized recommendations on the Netflix Homepage are based on a user's viewing habits and the behavior of similar users. These recommendations, organized for efficient browsing, enable users to discover the next great video to watch and enjoy without additional input or an explicit expression of their intents or goals. The Netflix Search experience, on the other hand, allows users to take active control of discovering new videos by explicitly expressing their entertainment needs via search queries. In this talk, we discuss the importance of producing search results that go beyond traditional keyword-matches to effectively satisfy users' search needs in the Netflix entertainment setting. Motivated by users' various search intents, we highlight the necessity to improve Search by applying approaches that have historically powered the Homepage. Specifically, we discuss our approach to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
