Predict movie ratings for all the users and thus recommend the relevant movies to the users making use Multifaceted Collaborative Filtering. This is realized by building a combined model that improves prediction accuracy by capitalizing on the advantages of both neighborhood and latent factor approaches.
On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.
Recommender systems are often based on Collaborative Filtering (CF) , which relies only on past user behavior—e.g., their previous transactions or product ratings—and does not require the creation of explicit profiles. Notably, CF techniques require no do- main knowledge and avoid the need for extensive data collection. In addition, relying directly on user behavior allows uncovering complex and unexpected patterns that would be difficult or impossible to profile using known data attributes. As a consequence, CF attracted much of attention in the past decade, resulting in significant progress and being adopted by some successful commercial systems,including Amazon, TiVo and Netflix.