Exploiting Diversification in Gossip-Based Recommendation

Abstract

In the context of Web 2.0, the users become massive producers of diverse data that can be stored in a large variety of systems. The fact that the users’ data spaces are distributed in many different systems makes data sharing difficult. In this context of large scale distribution of users and data, a general solution to data sharing is offered by distributed search and recommendation. In particular, gossip-based approaches provide scalability, dynamicity, autonomy and decentralized control. Generally, in gossip-based search and recommendation, each user constructs a cluster of “relevant” users that will be employed in the processing of queries. However, considering only relevance introduces a significant amount of redundancy among users. As a result, when a query is submitted, as the user profiles in each user’s cluster are quite similar, the probability of retrieving the same set of relevant items increases, and recall results are limited. In this paper, we propose a gossip-based search and recommendation approach that is based on a new clustering score, called usefulness, that combines relevance and diversity, and we present the corresponding new gossip-based clustering algorithm. We validate our proposal with an experimental evaluation using three datasets based on MovieLens, Flickr and LastFM. Compared with state of the art solutions, we obtain major gains with a three order of magnitude recall improvement when using the notion of usefulness regardless of the relevance score between two users used.

Work conducted within the Institut de Biologie Computationnelle and partially funded by the labex NUMEV and the CNRS project Mastodons.