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

Author

Listed:
  • Matteo Manca

    (Università di Cagliari)

  • Ludovico Boratto

    (Università di Cagliari)

  • Salvatore Carta

    (Università di Cagliari)

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.

Suggested Citation

  • Matteo Manca & Ludovico Boratto & Salvatore Carta, 2018. "Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system," Information Systems Frontiers, Springer, vol. 20(4), pages 825-839, August.
  • Handle: RePEc:spr:infosf:v:20:y:2018:i:4:d:10.1007_s10796-015-9600-3
    DOI: 10.1007/s10796-015-9600-3
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    References listed on IDEAS

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    Cited by:

    1. Ludovico Boratto & Salvatore Carta & Andreas Kaltenbrunner & Matteo Manca, 2018. "Guest Editorial: Behavioral-Data Mining in Information Systems and the Big Data Era," Information Systems Frontiers, Springer, vol. 20(6), pages 1153-1156, December.
    2. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.

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