Receiver's: Soure Calculate profile Trust Social network Social network ana Sources qualifications component Online Calculate communications trength (recommendation) Figure 1. Conceptual recommender system design based on our proposed framework. Rectangles represent input(red)or output(blue) information, trimmed rectangles(orange)represent system processes, and the green rectangle is the final output. Table 1 summarizes some relevant research establish reputation mechanisms based on ei- projects that explore the use of social approaches ther ratings of recommendations or on analysis to design recommender systems. of the social network ' s structure Research on social recommendation sys tems is in its early phases, and most current Figure 1 presents a conceptual design of a rec attempts to incorporate relationship informa- ommender system based on our proposed tion into recommender systems employ only a subset of the available indicators further- As Figure 1 shows, once the system records the more, it seems that the design choices in these various relationship indicators, the system source works are somewhat ad hoc and are often not qualification component calculates a weighted av informed by current knowledge and theories erage of the indicators to arrive at a single quali- of human behavior fication score for each source. We expect that the task domain(that is, leisure versus work-related Our Proposed Framework tasks)will affect the relative importance(weights) We propose a social recommendation framework of the various source qualification indicators that borrows from advice-taking theory by inte For example, based on results from behavioral grating the aforementioned relationship indica- studies, we expect that for movie recommenda tors between users and recommendation sources on tasks, users will deem shared preferences (homophily, tie strength, trust, and reputation). as more important than interaction frequency. A social recommender system based on this Next, the system prediction component takes framework would employ various mechanisms sources' qualifications and their history of rat- for capturing relationship information ings as input to predict an item's relevancy to the cipient and produces a recommendation. We track user consumption patterns, construct present an algorithm for a possible implementa- user profiles, and compare profiles(to detect tion of this framework in the sidebar "Algorithm for Implementing Our Framework. establish social networks and propagate links to form indirect links(to establish users' trust Using Social Relationship Data Alleviate the Cold-Start Problem record user communication patterns and inter- Because research on social recommender sys action frequency (as evidence for tie strength): tems is still in its infancy, both industry and academia have experiments currently in processcomputer.org/ITPro 41 Table 1 summarizes some relevant research projects that explore the use of social approaches to design recommender systems. Research on social recommendation sys￾tems is in its early phases, and most current attempts to incorporate relationship informa￾tion into recommender systems employ only a subset of the available indicators. Further￾more, it seems that the design choices in these works are somewhat ad hoc and are often not informed by current knowledge and theories of human behavior. Our Proposed Framework We propose a social recommendation framework that borrows from advice-taking theory by inte￾grating the aforementioned relationship indica￾tors between users and recommendation sources (homophily, tie strength, trust, and reputation). A social recommender system based on this framework would employ various mechanisms for capturing relationship information: • track user consumption patterns, construct user profiles, and compare profiles (to detect homophily, as in CF systems); • establish social networks and propagate links to form indirect links (to establish users’ trust in each other); • record user communication patterns and inter￾action frequency (as evidence for tie strength); and • establish reputation mechanisms based on ei￾ther ratings of recommendations or on analysis of the social network’s structure. Figure 1 presents a conceptual design of a rec￾ommender system based on our proposed framework. As Figure 1 shows, once the system records the various relationship indicators, the system source qualification component calculates a weighted av￾erage of the indicators to arrive at a single quali￾fication score for each source. We expect that the task domain (that is, leisure versus work-related tasks) will affect the relative importance (weights) of the various source qualification indicators. For example, based on results from behavioral studies, we expect that for movie recommenda￾tion tasks, users will deem shared preferences as more important than interaction frequency. Next, the system prediction component takes sources’ qualifications and their history of rat￾ings as input to predict an item’s relevancy to the recipient and produces a recommendation. We present an algorithm for a possible implementa￾tion of this framework in the sidebar “Algorithm for Implementing Our Framework.” Using Social Relationship Data to Alleviate the Cold-Start Problem Because research on social recommender sys￾tems is still in its infancy, both industry and academia have experiments currently in process Shared preferences System’s prediction component System’s prediction (recommendation) System’s source qualification component Calculate profile similarity Source’s qualifications Social network Trust propagation Trust Ratings of recommendation Reputation mechanisms Social network analysis Source’s reputation Online communications Calculate interaction frequency Tie strength Consumption history Receiver’s Source’s Figure 1. Conceptual recommender system design based on our proposed framework. Rectangles represent input (red) or output (blue) information, trimmed rectangles (orange) represent system processes, and the green rectangle is the final output