Profile Diversity for P2P Search and Recommendation

We investigate profile diversity for P2P search and recommendation of scientific documents. In scientific domains, endorsements from different communities are important indicators of the broad focus of scientific documents and should be accounted for in search and recommendation. To do so, we introduce profile diversity, a novel idea in searching scientific documents. Traditional content diversity has been thoroughly studied in centralized search and advertising, database queries, and recommendations and addresses the question of returning relevant but too-similar documents. We argue that content diversity alone does not suffice for finding documents endorsed by different scientific communities and that profile diversity is needed to alleviate returning popular but too-focused documents. Moreover, P2P profile diversity increases recall and reduces the search space compared with a centralized approach. We believe this paper is the first to investigate P2P profile diversity in search and recommendation and to study its various facets: architecture, scoring function, and algorithms. Our experiments on the TREC09 benchmark validate our proposal