The Netflix Recommender System

Abstract

This article discusses the various algorithms that make up the Netflix recommender system, and describes its business purpose. We also describe the role of search and related algorithms, which for us turns into a recommendations problem as well. We explain the motivations behind and review the approach that we use to improve the recommendation algorithms, combining A/B testing focused on improving member retention and medium term engagement, as well as offline experimentation using historical member engagement data. We discuss some of the issues in designing and interpreting A/B tests. Finally, we describe some current areas of focused innovation, which include making our recommender system global and language aware.

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

“…Many BD and DL applications have emerged to achieve better searches in e-commerce stores, which help you to find a product more effectively [100], recommendation engines that suggest the most relevant items to users [101,102], or personalized advertising to target the most receptive audiences [103]. In the media, DL is helping Netflix to predict what customers will enjoy watching, and this company also uses data and prediction to drive what new content it would better invest in creating [104].…”

Jumping, dumping, and pumping: Three mental principles for idea generation to activate software-based tools in business model innovation

“…In the following, we briefly elaborate on the business model of Netflix, a firm that has used business model innovation consciously and successfully over the past years and is well known for its streaming service available in almost all countries across the globe (Abraham, 2013;Weill & Woerner, 2013;Gomez-Uribe & Hunt, 2016). In the remainder of this study, we will refer to Netflix's business model for illustration.…”

“…In this way, Netflix introduced nonestablished and unusual mechanisms into the movie rental industry. For example, it replaced pay-per-use by a monthly subscription (revenue stream), introduced on-demand streaming (value proposition), significantly extended the variety of content (value proposition), invented algorithms for their recommender system based on a data-driven analysis of customer needs, which pro-actively suggests movies (key resource), improved accessibility by allowing customer to use different devices (channels), and in a more recent development, even started producing films itself (key activity) (Abraham, 2013;Weill & Woerner, 2013;Gomez-Uribe & Hunt, 2016).…”

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“…Al diseñar desde su arquitectura la aplicación para que el sistema de recomendación opere de forma dinámica, se le proporciona al sistema la capacidad de presentar recomendaciones en tiempo real y aprovechar el pequeño intervalo de tiempo en el que el cliente es receptivo a las sugerencias y muestra interés en adquirir nuevos productos como se sugiere en el artículo [3] (sección I).…”

“…Ahora mismo, pequeñas y grandes empresas han optado por incluir, dentro de sus portafolios de servicios, una gran variedad de avances tecnológicos con el objetivo de mejorar su utilidad, y lo más importante, fidelizar al cliente con experiencias personalizadas [2]. Para Netflix, una empresa comercial estadounidense, el cliente pierde el interés en la bús-queda de contenido digital (películas), pasados los 60 a 90 segundos de búsqueda, lo que trae consigo el abandono del servicio y, por lo tanto, la pérdida del cliente [3].…”