Interest-Based vs. Social Person-Recommenders in Social Networking Platforms

Social network based approaches to person recommendations are compared to interest based approaches with the help of an empirical study on a large German social networking platform. We assess and compare the performance of different basic variants of the two approaches by precision / recall based performance with respect to reproducing known friendship relations and by an empirical questionnaire based study. In accordance to expectation, the results show that interest based person recommenders are able to produce more novel recommendations while performing less well with respect to friendship reproduction. With respect to the user's assessment of recommendation quality all approaches perform comparably well, while combined social-interest-based variants are slightly ahead in performance. The overall results qualify those combined approaches as a good compromise.

[1]  E. Noelle-Neumann The Theory of Public Opinion: The Concept of the Spiral of Silence , 1991 .

[2]  Kate Ehrlich,et al.  Searching for experts in the enterprise: combining text and social network analysis , 2007, GROUP.

[3]  Marti A. Hearst,et al.  Assessing attractiveness in online dating profiles , 2008, CHI.

[4]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[5]  Georg Groh,et al.  Recommendations in taste related domains: collaborative filtering vs. social filtering , 2007, GROUP.

[6]  David W. McDonald,et al.  Recommending collaboration with social networks: a comparative evaluation , 2003, CHI '03.

[7]  Jennifer Golbeck,et al.  Computing and Applying Trust in Web-based Social Networks , 2005 .

[8]  Licia Capra,et al.  Social ranking: uncovering relevant content using tag-based recommender systems , 2008, RecSys '08.

[9]  Mark S. Ackerman,et al.  Expertise recommender: a flexible recommendation system and architecture , 2000, CSCW '00.

[10]  Stephen Farrell,et al.  Harvesting with SONAR: the value of aggregating social network information , 2008, CHI.

[11]  Sihem Amer-Yahia,et al.  Relevance and ranking in online dating systems , 2010, SIGIR.

[12]  Michele Brocco,et al.  A Meta Model for Team Recommendations in Open Innovation Networks , 2012 .

[13]  Charles E. Miller,et al.  Group decision making and normative versus informational influence: Effects of type of issue and assigned decision rule. , 1987 .

[14]  David W. McDonald,et al.  Social matching: A framework and research agenda , 2005, TCHI.

[15]  Wolfgang Woerndl,et al.  Recommending for Groups in Decentralized Collaborative Filtering , 2009 .

[16]  M. A. Sasse,et al.  ’Knowing me, knowing you’ — Using profiles and social networking to improve recommender systems , 2006 .

[17]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[18]  Shinsuke Nakajima,et al.  Tag-Based Contextual Collaborative Filtering , 2007 .

[19]  Jessica Friedman,et al.  CommunitySpace: Toward Flexible Support for Voluntary Knowledge Communities , 1999 .

[20]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[21]  Michael J. Muller,et al.  Make new friends, but keep the old: recommending people on social networking sites , 2009, CHI.