Profile Diversity in Search and Recommendation

Maximilien Servajean 1, 2 Esther Pacitti 1, 2 Sihem Amer-Yahia 3 Pascal Neveu 4
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
Abstract : We investigate profile diversity, a novel idea in searching scientific documents. Combining keyword relevance with popularity in a scoring function has been the subject of different forms of social relevance. Content diversity has been thoroughly studied in search and advertising, database queries, and recommendations. We believe our work is the first to investigate profile diversity to address the problem of returning highly popular but too-focused documents. We show how to adapt Fagin's threshold-based algorithms to return the most relevant and most popular documents that satisfy content and profile diversities and run preliminary experiments on two benchmarks to validate our scoring function.
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Submitted on : Monday, June 3, 2013 - 9:46:05 AM
Last modification on : Friday, March 15, 2019 - 1:15:01 AM
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Maximilien Servajean, Esther Pacitti, Sihem Amer-Yahia, Pascal Neveu. Profile Diversity in Search and Recommendation. SRS: Social Recommender Systems (in conjunction WWW 2013), May 2013, Rio de Janeiro, Brazil. pp.973-980. ⟨lirmm-00806676⟩

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