Novel Hybrid Approach to Content Recommendation Based on Predicted Profiles

The present phenomenon of technology convergence is blurring away the frontiers between the Internet and the TV, operating a shift on the way TV is consumed. TV viewers have now access to a huge selection of TV programming as well as online contents, either previously broadcasted or natively produced for the Internet. This reality creates new necessities whilst opening new opportunities for the creation of services capable of filtering this information and presenting the user with the most relevant content. This article describes an innovative hybrid strategy for delivering recommendations of TV content to individual users. It was developed specifically for the TV entertainment services of hotels, but it can be applied to any multimedia consumption service. Without requiring users to explicitly rate the programs they have watched, it is still able to recommend similar programs to similar users. It adopts an improved Pearson correlation method to establish similarities between different users, comparing profiles that have been automatically generated based on the user viewing history. It builds a predicted user profile, which is then used within a content-based approach to generate recommendations.