UC San Diego

As online retailers, media providers, and others have learned, the ability to provide quality recommendations for users is a strong profit multiplier: it drives sales, maintains retention, and provides value for users. Video streaming, retail, and video databases particularly have had extensive effort and research devoted to finding effective ways to make representations. One of these is MovieLens.org, a video recommender website that uses a representation called the tag genome to understand movies and make tuned recommendations. The tag genome representation is built from user input on the website, where users can apply tags to movies and rate them in order to provide the information needed to make recommendations.

In this work, we draw from research in information retrieval to implement a bag-of-concepts representation for movies and make tuned recommendations in the same manner as MovieLens. Our implementation is fully unsupervised and does not require the user data needed in the implementation of MovieLens while still having similar properties to the tag genome that enable interesting tuned recommendations.