Personalized social search based on the user's social network

  • Authors:

  • David Carmel;Naama Zwerdling;Ido Guy;Shila Ofek-Koifman;Nadav Har'el;Inbal Ronen;Erel Uziel;Sivan Yogev;Sergey Chernov

  • Affiliations:

  • IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;IBM Research Lab in Haifa, Haifa, Israel;Leibniz University, Hanover, Germany

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

This work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people "similar" to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user's related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy.