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Network-Based Models for Social Recommender Systems

Godoy-Lorite, A; Guimerà, R; Sales-Pardo, M; (2019) Network-Based Models for Social Recommender Systems. In: Moscato, P and De Vries, NJ, (eds.) Business and Consumer Analytics: New Ideas. (pp. 491-512). Springer: Cham, Switzerland. Green open access

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With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modelling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets.

Type: Book chapter
Title: Network-Based Models for Social Recommender Systems
ISBN-13: 978-3-030-06221-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-06222-4_11
Publisher version: https://doi.org/10.1007/978-3-030-06222-4_11
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Human preferences, Prediction, Matrix factorization, Modelling, Social marketing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10111685
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