Artwork personalization at netflix

For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.

Counterfactual RiskMinimizationCounterfactual Risk MinimizationMeasuring the BusinessValue of Recommender…Measuring the Business Value of Recommender SystemsThe Mutual Domesticationof Users and Algorithmi…The Mutual Domestication of Users and Algorithmic Recommendations on NetflixInnovation and Design inthe Age of Artificial…Innovation and Design in the Age of Artificial IntelligenceTowards ExplainablePersonalized…Towards Explainable Personalized Recommendations by Learning from Users' PhotosRecommender SystemsLeveraging Multimedia…Recommender Systems Leveraging Multimedia ContentQuantifying theseparability of data…Quantifying the separability of data classes in neural networksTesting the Plasticityof Reinforcement…Testing the Plasticity of Reinforcement Learning-based SystemsAn Image is Worth OneWord: Personalizing…An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual InversionEncoder-based DomainTuning for Fast…Encoder-based Domain Tuning for Fast Personalization of Text-to-Image ModelsDomain-AgnosticTuning-Encoder for Fast…Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image ModelsTesting of DeepReinforcement Learning…Testing of Deep Reinforcement Learning Agents with Surrogate ModelsAnti-DreamBooth:Protecting users from…Anti-DreamBooth: Protecting users from personalized text-to-image synthesisArtwork personalizationat netflixArtwork personalization at netflixEarlier referencesFocus paperCiting papersOlderNewer

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