Commercial Social Recommender Systems ince the introduction of collaborative filtering matching users with movies and using these recom- (CF)algorithms in the mid-1990s, social-based mendations to push items on the long-tail portion recommendation techniques have played a significant of its inventory. In addition to CF, Netflix lets users role in shaping consumer Web-based recommenda- define a social network of friends, allowing them to tion applications view each other's preferences. However, this social The first large-scale implementation of CF is at- network data isn't incorporated into Netflix's CF tributed to Amazon. com which launched its book algorithm yet recommendation application in 1995. It later extend- Epinions. com is a successful product recommenda ed recommendations to additional products, such as tion site launched in 1999 to let users rate products music CDs and consumer goods. Amazon has been a its CF algorithm then uses these ratings to make leader in adopting social approaches to recommenda- product recommendations. Additionally, users can tions, and it provided user reviews for its products at associate themselves with others whose opinions an early stage. Recently, Amazon upgraded its review they trust. Epinions then forms a"web of trust, system to incorporate user ratings of reviews and a propagating this trust information across a networ reputation system that establishes reviewer credibility. and incorporating it into its CF algorithm. Thus, Netflix, a Web-based movie rental service, relies Epinions is a pioneer in developing a social recom- heavily on its CF system to recommend movies to mender system that incorporates two types of social users. The company has been extremely effective at relations: shared preferences and trust. that the design of systems should be grounded mendations phily-particularly, similarity in theoretical foundations. In the context of rec- in knowledg references-is a key deter- ommender systems, we believe that designers minant of a recipient would accept a should consider behavioral theories of persua- sources advice, specifically in dom nains suo sion and advice taking when they design social movie and book recommendations recommender systems. Although the design of From a system design perspective, we could es existing CF systems assumes that similarities in timate similarity in preferences between various preferences (as captured in users' consumption users by recording their consumption patterns profiles) determine recommendation quality, be- and comparing these patterns. Early recommend havioral theory suggests that other characteris- er systems adopted this CF approach, which has tics-such as the source's trustworthiness and uickly become the industry standard(an example Or Putation-determine the recipient's perception is Amazons recommender system). This approach works well for large user communities where suf- ficient information is available about each user Should i take your advice? Recent CF research provides enhancements along Online relationships are useful for a variety various dimensions, such as automatically elicit- of purposes, including social (such as those in ing accurate user feedback, employing algorithms My Space), job searching(LinkedIn), informa- to measure users' similarities, and improving tion access(Slashdot. org), and commerce (eBay). prediction methods. The main advantage of this Although these online ties weren't established approach is that it requires little effort from us- for the purpose of advice taking, recommender ers: they might need to rate the items theyve con- systems could use them to link a user with rel- sumed, but they aren ' t required to explicitly define evant sources Using previous research in mar- their relationships to other users. Its limitation is keting, applied psychology, and organization, we that in cases where little information is available identified four salient constructs that impact about users and items (referred to as a cold start) recipient's advice-taking decision-homophily, prediction accuracy suffers tie strength, trust, and social capital. We argue Behavioral researchers have studied tie that these constructs are relevant for the design strength-the intensity of the relationship be of recommender systems tween the recipient and source-and identified it Homophily refers to the similarity between as a key determinant in a recipient's likelihood source and recipient, and marketing research to take advice. Tie strength has several facets has investigated it for word-of-mouth recom- including the relationships duration, interaction omputer. org/ITProcomputer.org/ITPro 3 9 that the design of systems should be grounded in theoretical foundations. In the context of rec￾ommender systems, we believe that designers should consider behavioral theories of persua￾sion and advice taking when they design social recommender systems. Although the design of existing CF systems assumes that similarities in preferences (as captured in users’ consumption profiles) determine recommendation quality, be￾havioral theory suggests that other characteris￾tics—such as the source’s trustworthiness and reputation—determine the recipient’s perception of the recommendation. Should I Take Your Advice? Online relationships are useful for a variety of purposes, including social (such as those in MySpace), job searching (LinkedIn), informa￾tion access (Slashdot.org), and commerce (eBay). Although these online ties weren’t established for the purpose of advice taking, recommender systems could use them to link a user with rel￾evant sources. Using previous research in mar￾keting, applied psychology, and organization, we identified four salient constructs that impact a recipient’s advice-taking decision—homophily, tie strength, trust, and social capital. We argue that these constructs are relevant for the design of recommender systems. Homophily refers to the similarity between source and recipient, and marketing research has investigated it for word-of-mouth recom￾mendations. Homophily—particularly, similarity in knowledge and preferences—is a key deter￾minant of whether a recipient would accept a source’s advice,2 specifically in domains such as movie and book recommendations. From a system design perspective, we could es￾timate similarity in preferences between various users by recording their consumption patterns and comparing these patterns. Early recommend￾er systems adopted this CF approach,3 which has quickly become the industry standard (an example is Amazon’s recommender system). This approach works well for large user communities where suf￾ficient information is available about each user. Recent CF research provides enhancements along various dimensions, such as automatically elicit￾ing accurate user feedback, employing algorithms to measure users’ similarities, and improving prediction methods.4 The main advantage of this approach is that it requires little effort from us￾ers: they might need to rate the items they’ve con￾sumed, but they aren’t required to explicitly define their relationships to other users. Its limitation is that in cases where little information is available about users and items (referred to as a cold start), prediction accuracy suffers. Behavioral researchers have studied tie strength—the intensity of the relationship be￾tween the recipient and source—and identified it as a key determinant in a recipient’s likelihood to take advice.5 Tie strength has several facets, including the relationship’s duration, interaction Commercial Social Recommender Systems Since the introduction of collaborative filtering (CF) algorithms in the mid-1990s, social-based recommendation techniques have played a significant role in shaping consumer Web-based recommenda￾tion applications. The first large-scale implementation of CF is at￾tributed to Amazon.com, which launched its book recommendation application in 1995. It later extend￾ed recommendations to additional products, such as music CDs and consumer goods. Amazon has been a leader in adopting social approaches to recommenda￾tions, and it provided user reviews for its products at an early stage. Recently, Amazon upgraded its review system to incorporate user ratings of reviews and a reputation system that establishes reviewer credibility. Netflix, a Web-based movie rental service, relies heavily on its CF system to recommend movies to users. The company has been extremely effective at matching users with movies and using these recom￾mendations to push items on the long-tail portion of its inventory. In addition to CF, Netflix lets users define a social network of friends, allowing them to view each other’s preferences. However, this social network data isn’t incorporated into Netflix’s CF algorithm yet. Epinions.com is a successful product recommenda￾tion site launched in 1999 to let users rate products; its CF algorithm then uses these ratings to make product recommendations. Additionally, users can associate themselves with others whose opinions they trust. Epinions then forms a “web of trust,” propagating this trust information across a network and incorporating it into its CF algorithm. Thus, Epinions is a pioneer in developing a social recom￾mender system that incorporates two types of social relations: shared preferences and trust