Online selection of mediated and domain-specific predictions for improved recommender systems
Stephanie L. Rosenthal, Manuela Veloso, Anind K. Dey · 2009
Recommender systems use a set of reviewers and advice givers with the goal of providing accurate userdependent product predictions. In general, these systems assign weights to different reviewers as a function of their similarity to each user. As products are known to be from different domains, a recommender system also considers product domain information in its predictions. As there are few reviews compared to the number of products, it is often hard to set the similarity-based weights as there is not a large enough subset of reviewers who reviewed the same products. It has then been recently suggested that not considering domains will increase the amount of reviewer data and the overall prediction accuracy in a mediated way. However, clearly, if different reviewers are similar to a user in each product domain, then domain-specific predictions could be superior to mediated ones. In this paper, we consider two advice giver algorithms to provide domain-specific and mediated predictions. We analyze both advice giver algorithms using large real data sets to characterize when each is more accurate for users. We realize that for a considerable number of users, the domain-specific predictions are possible and more accurate. We then contribute an improved general recommender system algorithm that autonomously selects the most accurate mediated or domain-specific advice giver for each user. We validate our analysis and algorithm using real data sets and show the improved predictions for different users.