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Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

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Abstract

In the last few years, social media systems have experienced a fast growth. The amount of content shared in these systems increases fast, leading users to face the well known “interaction overload” problem, i.e., they are overwhelmed by content, so it becomes difficult to come across interesting items. To overcome this problem, social recommender systems have been recently designed and developed in order to filter content and recommend to users only interesting items. This type of filtering is usually affected by the “over-specialization” problem, which is related to recommendations that are too similar to the items already considered by the users. This paper proposes a friend recommender system that operates in the social bookmarking application domain and is based on behavioral data mining, i.e., on the exploitation of the users activity in a social bookmarking system. Experimental results show how this type of mining is able to produce accurate friend recommendations, allowing users to get to know bookmarked resources that are both novel and serendipitous. Using this approach, the impact of the “interaction overload” and the “over-specialization” problems is strongly reduced.

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  1. http://www.delicious.com

  2. http://www.citeulike.org/

  3. http://www.flickr.com/

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Cagliari, Via Ospedale 72, 09124, Cagliari, Italy

    Matteo Manca, Ludovico Boratto & Salvatore Carta

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  1. Matteo Manca
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  2. Ludovico Boratto
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  3. Salvatore Carta
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Correspondence to Ludovico Boratto.

Additional information

This work is partially funded by Regione Sardegna under project SocialGlue, through PIA - Pacchetti Integrati di Agevolazione “Industria Artigianato e Servizi” (annualità 2010), and by MIUR PRIN 2010-11 under project “Security Horizons”.

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Manca, M., Boratto, L. & Carta, S. Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system. Inf Syst Front 20, 825–839 (2018). https://doi.org/10.1007/s10796-015-9600-3

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