A Theory-Driven Design Framework for Social Recommender Systems

Journal of the Association for Information Systems, Vol. 11, No. 9, pp. 455-490, September 2010

University of Alberta School of Business Research Paper No. 2013-103

Posted: 23 May 2013 Last revised: 27 Jun 2013

See all articles by Ofer Arazy

Ofer Arazy

Independent

Nanda Kumar

CUNY Baruch College - CIS, Zicklin School of Business

Bracha Shapira

Ben-Gurion University of the Negev

Date Written: September 1, 2009

Abstract

Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.

Suggested Citation

Arazy, Ofer and Kumar, Nanda and Shapira, Bracha, A Theory-Driven Design Framework for Social Recommender Systems (September 1, 2009). Journal of the Association for Information Systems, Vol. 11, No. 9, pp. 455-490, September 2010, University of Alberta School of Business Research Paper No. 2013-103, Available at SSRN: https://ssrn.com/abstract=2268690

Nanda Kumar

CUNY Baruch College - CIS, Zicklin School of Business ( email )

17 Lexington Avenue
New York, NY 10010
United States

HOME PAGE: http://cisnet.baruch.cuny.edu/kumar

Bracha Shapira

Ben-Gurion University of the Negev ( email )

Beer-Sheva
Israel

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