My library button
  • No image available

    Recommender systems are a valuable means for online users to cope with the virtual information overload. Development of them is a multi-disciplinary effort, and this book covers all aspects of, and important techniques for, recommender systems.

  • No image available

    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.

  • No image available

    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.

  • No image available

  • No image available

    Opinion leaders influence the decisions of others and play a significant role in disseminating information, specifically in the domain of e-commerce. Prior studies exploring the factors that affect a person's ability to influence others have been conducted in either a work setting (i.e. advice networks) or leisure setting (e.g. movie recommendation). However, it is common for these networks to interweave, for instance when a person asks for advice from work colleagues on a personal issue, like the purchase of a car. This suggests that there is a need to differentiate between the antecedents of opinion leadership that stem from one's position in the professional network and the antecedents that stem from personal characteristics associated with the specific non-work related advice (e.g. expertise in cars). To explore how opinion leadership is determined in such multifaceted settings, we develop a theoretical framework of opinion leadership. The results from an empirical study of a movie advice task that was conducted in a professional setting, demonstrate that both movie-related trustworthiness and work-related centrality exert distinct effects on one's ability to influence others opinions regarding movies. Implications for theory and practice of e-commerce are discussed.

  • No image available