SOCIAL COMPUTINGI mproving Social Recommender d Systems Ofer Arazy, University of Alberta Nanda Kumar, City University of New York Bracha Shapira, Deutsche Telekom Laboratories at Ben-Gurion University Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools R ecommender systems are a key compo- Today, online communities-with their strong nent of successful online stores such ties and built-in relationships-present an op as Amazon. com, Epinions. com, and portunity for enhancing the design of social Netflix as they help users sort through recommender systems and increasing system pre a site and find relevant information(we discuss diction accuracy. We can use the various relation the approach behind each of these examples in ships captured in these communities(phrased the"Commercial Social Recommender Systems"trust"on Epinions and"reputation"on eBay)in sidebar). Since the emergence of social (or collab- new ways, by incorporating better indicators of orative)filtering techniques in the mid-1990s, the relationship information. The potential impact of industry has adopted a wide variety of collabora- these social recommender systems is not restricted tive filtering(CF)designs to generate recommen- to the public domain: the recent advent of Enter dations. Typically, CF works by identifying recom- prise 2. 0-the application of Web 2.0 approaches mendation sources with preferences similar to the in enterprises-is expected to bring social recom user, identifying items that these sources like(but mendation techniques to corporate settings which the user hasn't purchased yet), predicting In this article, we present a framework for so the relevance of these items(based on ratings and cial recommender systems that is intended to the source's similarity to the user), and recom- hance recommendation accuracy We model our ending the most relevant items approach after Arazy and Woo, who proposed rTPr。 July/August2009 20-9202/09s25.00@2009lEEE38 IT Pro July/August 2009 Published by the IEEE Computer Society 1520-9202/09/$25.00 © 2009 IEEE SoCIAl CompuTINg Improving Social Recommender Systems Ofer Arazy, University of Alberta Nanda Kumar, City University of New York Bracha Shapira, Deutsche Telekom Laboratories at Ben-Gurion University Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools. Recommender systems are a key compo￾nent of successful online stores such as Amazon.com, Epinions.com, and Netflix as they help users sort through a site and find relevant information (we discuss the approach behind each of these examples in the “Commercial Social Recommender Systems” sidebar). Since the emergence of social (or collab￾orative) filtering techniques in the mid-1990s, the industry has adopted a wide variety of collabora￾tive filtering (CF) designs to generate recommen￾dations. Typically, CF works by identifying recom￾mendation sources with preferences similar to the user, identifying items that these sources like (but which the user hasn’t purchased yet), predicting the relevance of these items (based on ratings and the source’s similarity to the user), and recom￾mending the most relevant items. Today, online communities—with their strong ties and built-in relationships—present an op￾portunity for enhancing the design of social recommender systems and increasing system pre￾diction accuracy. We can use the various relation￾ships captured in these communities (phrased as “trust” on Epinions and “reputation” on eBay) in new ways, by incorporating better indicators of relationship information. The potential impact of these social recommender systems is not restricted to the public domain: the recent advent of Enter￾prise 2.0—the application of Web 2.0 approaches in enterprises—is expected to bring social recom￾mendation techniques to corporate settings. In this article, we present a framework for so￾cial recommender systems that is intended to en￾hance recommendation accuracy. We model our approach after Arazy and Woo,1 who proposed © Terhox | Dreamstime.com