Table 2. Effort and privacy considerations for extracting relationship indicators Evidence System administration effort Privacy concerns Shared preferences Low(if based on purchase Low(existing CF available Low (only rating of items) history) or medium(when ratings of items are required) Communication Low(monitoring electronic Medium(social relations communication Social network--direct gh(establishing a social Low(social network) Medium(social relations) elations Social network-indirect High(establishing a social Medium(social network and Medium(social relations) network trust High(establishing a social Medium(social network and Medium(social relations) network analysis(SNA) network SNA calculations) Reputation system Medium(rating of others High(reputation mechanism Low(rating of others recommendations) and fraud control) a social network using SNA, but implementing a or more than a decade now the ad hoc reputation mechanism requires setting up tech- standard in recommendation systems has nical and social controls to combat fraud and as- F been based on users' shared preferences sure normative user behavior Recent advances in academia and industry sug Privacy is a major issue for both users and gest that we can employ alternative sources of re system administrators. Users are reluctant to lationship information to enhance recommender provide personal details for fear of misuse, and system performance. By considering these dif- legal issues associated with protecting user pri- behavioral theory, we propose a comPac o system administrators are concerned about the ferent approaches and grounding our analysi vacy. Calculating shared preferences requires sign for a social recommender system that has the tracking consumption data and gathering data potential to alleviate the cold-start problem and about consumed item ratings(whenever collect- improve recommendation accuracy. We hope that ed). Tracking communication frequency, as well others will investigate similar approaches to em- as collecting social network data, might pose a ploy social relationship information in the design larger threat to privacy because users might con- of recommender systems. Notwithstanding the sider their social relations with others to be con- potential benefits, our approach has some limi- fidential information. However, the information tations associated with administration costs, us- that reputation systems use-ratings of recom- ability, and user privacy. In implementing social mendations and reputation scores-is often con- recommender systems and choosing which types sidered public knowledge of relationship indicators to employ, system de The analysis we mention here highlights the signers should consider the risks associated with advantages of the shared-preferences approach in each indicator. light of user effort and privacy concerns. Never- theless, the use of additional indicators for social relationships has potential benefits. First, in- References corporating additional information sources will 1. O Arazy and C Woo, "Enhancing Information Re ackle the cold-start problem and increase pre- trieval through Statistical Natural Language Process- diction reliability. Second, even in cases where ing: A Study of Collocation Indexing, "Management shared preference scores are reliable we need information Systems Quarterly, vol 31, no. 3, 2007, PP to incorporate additional indicators of social re 525-546 esau se behavioral theory suggest 2. M. Gilly et al., "A Dyadic Study of Interpersonal In that shared preferences are just one of several formation Search, "J. Academy of Marketing Science, voL. factors that determine a recipient's likelihood of accepting advice. Moreover, extracting relation- 3. U Shardanand and P Maes, "Social Information Fil- ship indicators might not require much effort tering: Algorithms for Automating Word of Mouth from users, especially if we can harvest this in- Proc. Conf. Human Factors in Computing Systems, ACM formation from existing online social networks Press,,1995,pp.210-217computer.org/ITPro 4 3 a social network using SNA, but implementing a reputation mechanism requires setting up tech￾nical and social controls to combat fraud and as￾sure normative user behavior. Privacy is a major issue for both users and system administrators. Users are reluctant to provide personal details for fear of misuse, and system administrators are concerned about the legal issues associated with protecting user pri￾vacy. Calculating shared preferences requires tracking consumption data and gathering data about consumed item ratings (whenever collect￾ed). Tracking communication frequency, as well as collecting social network data, might pose a larger threat to privacy because users might con￾sider their social relations with others to be con￾fidential information. However, the information that reputation systems use—ratings of recom￾mendations and reputation scores—is often con￾sidered public knowledge. The analysis we mention here highlights the advantages of the shared-preferences approach in light of user effort and privacy concerns. Never￾theless, the use of additional indicators for social relationships has potential benefits. First, in￾corporating additional information sources will tackle the cold-start problem and increase pre￾diction reliability. Second, even in cases where shared preference scores are reliable, we need to incorporate additional indicators of social re￾lationships because behavioral theory suggests that shared preferences are just one of several factors that determine a recipient’s likelihood of accepting advice. Moreover, extracting relation￾ship indicators might not require much effort from users, especially if we can harvest this in￾formation from existing online social networks. F or more than a decade now, the ad hoc standard in recommendation systems has been based on users’ shared preferences. Recent advances in academia and industry sug￾gest that we can employ alternative sources of re￾lationship information to enhance recommender system performance. By considering these dif￾ferent approaches and grounding our analysis in behavioral theory, we propose a conceptual de￾sign for a social recommender system that has the potential to alleviate the cold-start problem and improve recommendation accuracy. We hope that others will investigate similar approaches to em￾ploy social relationship information in the design of recommender systems. Notwithstanding the potential benefits, our approach has some limi￾tations associated with administration costs, us￾ability, and user privacy. In implementing social recommender systems and choosing which types of relationship indicators to employ, system de￾signers should consider the risks associated with each indicator. References 1. O. Arazy and C. Woo, “Enhancing Information Re￾trieval through Statistical Natural Language Process￾ing: A Study of Collocation Indexing,” Management Information Systems Quarterly, vol. 31, no. 3, 2007, pp. 525–546. 2. M. Gilly et al., “A Dyadic Study of Interpersonal In￾formation Search,” J. Academy of Marketing Science, vol. 26, no. 2, 1998, pp. 83–100. 3. U. Shardanand and P. Maes, “Social Information Fil￾tering: Algorithms for Automating Word of Mouth,” Proc. Conf. Human Factors in Computing Systems, ACM Press, 1995, pp. 210–217. Table 2. Effort and privacy considerations for extracting relationship indicators. Evidence User effort System administration effort Privacy concerns Shared preferences Low (if based on purchase Low (existing CF available) Low (only rating of items) history) or medium (when ratings of items are required) Communication Low (automatic) Low (monitoring electronic Medium (social relations) frequency communication) Social network—direct High (establishing a social Low (social network) Medium (social relations) relations network) Social network—indirect High (establishing a social Medium (social network and Medium (social relations) relations network) trust propagation) Social network—social High (establishing a social Medium (social network and Medium (social relations) network analysis (SNA) network) SNA calculations) Reputation system Medium (rating of others’ High (reputation mechanism Low (rating of others’ recommendations) and fraud control) recommendations)