Peersommender: A peer-level annotation based approach for multimedia recommendation

Abstract

In this paper, we propose the Peersommender architecture, which is the set of applications that provide personalized content to users according to their annotations produced when watching multimedia items. As opposite to hierarchical authoring, which is metadata created by experts to describe content in an organized, structured and impartial manner, peer-level annotations are highly personal because they are created by consumers, and this feature can be used to infer relevant content that is of interest to the user. In this work, in particular, we propose a movie recommender system that explores a user profile with automatic augmentation, which is based on annotations produced by him/her in the past. By combining tags, faces of interest and ratings with usual hierarchical metadata, we are able to predict ratings for new movies based on a enhanced hybrid approach for content filtering. Our evaluation was executed over a large scale dataset containing real users, and it shows good results when compared to other techniques.