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Increasing Coverage in Distributed Search and Recommendation with Profile Diversity

Maximilien Servajean 1 Esther Pacitti 2, 1 Miguel Liroz-Gistau 1 Sihem Amer-Yahia 3 Amr El Abbadi 4
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : With the advent of Web 2.0 users are producing bigger and bigger amounts of diverse data, which are stored in a large variety of systems. Since the users’ data spaces are scattered among those independent systems, data sharing becomes a challenging problem. Distributed search and recommendation provides a general solution for data sharing and among its various alternatives, gossip-based approaches are particularly interesting as they provide scalability, dynamicity, autonomy and decentralized control. Generally, in these approaches each participant maintains a cluster of “relevant” users, which are later employed in query processing. However, as we show in the paper, only considering relevance in the construction of the cluster introduces a significant amount of redundancy among users, which in turn leads to reduced recall. Indeed, when a query is submitted, due to the high similarity among the users in a cluster, the probability of retrieving the same set of relevant items increases, thus limiting the amount of distinct results that can be obtained. In this paper, we propose a gossip-based search and recommendation approach that is based on diversity-based clustering scores. We present the resultant new gossip-based clustering algorithms and validate them through experimental evaluation over four real datasets, based on MovieLens-small, MovieLens, LastFM and Delicious. Compared with state of the art solutions, we show that taking into account diversity-based clustering score enables to obtain major gains in terms of recall while reducing the number of users involved during query processing.
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Contributor : Maximilien Servajean <>
Submitted on : Friday, July 17, 2015 - 10:50:51 AM
Last modification on : Tuesday, December 8, 2020 - 10:38:02 AM



Maximilien Servajean, Esther Pacitti, Miguel Liroz-Gistau, Sihem Amer-Yahia, Amr El Abbadi. Increasing Coverage in Distributed Search and Recommendation with Profile Diversity. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer Berlin / Heidelberg, 2015, LNCS (9430), pp.115-144. ⟨10.1007/978-3-662-48567-5_4⟩. ⟨lirmm-01177817⟩



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