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Personalized recommendations on the Netflix Homepage are based on a users 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 leverage recommendations in the context of Search and to effectively organize search results to provide a product experience that meaningfully adds value for our users.
We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search context to aid content discovery and support searches for unavailable
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could lead to suboptimal recommendation quality and training efficiency. Some
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) and meaningfully transform them in the low-dimensional space. The major
Search conducted in a work context is an everyday activity that has been around since long before the Web was invented, yet we still seem to understand little about its general characteristics. With this paper we aim to contribute to a better underst
Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming