Multi-List Recommendations for Personalizing Streaming Content

Abstract

The decision behind choosing a recommender system that yields accurate recommendations yet allows users to explore more content has been a topic of research in the last decades. This work attempts to find a recommender system for TV 2 Play, a movie streaming platform, that would perform well on implicit feedback data and provide multi-lists as recommenda- tions. Several approaches are examined for suitability, and Collaborative Filtering and Multi- Armed Bandits are decided upon. The models for each approach are built using the pipeline utilized by TV 2 Play. The models are then compared in performance on several evaluation metrics in the first stage of offline testing, yielding Alternating Least Squares and Bayesian Personalized Ranking as the best-performing models. The second stage of offline testing includes testing the two models and their variants with the BM25 weighting scheme applied against each other. The unweighted Bayesian Personalized Ranking model has shown the highest user-centric metrics while maintaining relatively high recommendation-centric met- rics, which led to that model being tested in online settings against the algorithm currently used by TV 2 Play team. The online testing has revealed that our model underperforms compared to the TV 2 Play model when used on the kids’ page but produces equally good results on the movies page. The results can be attributed to the differences in behavioral content consumption patterns between users.