Figure 1: User can choose between Online recommender and social recommendations(from friends) Social commendations Input from user Output (Recommendations) Online recommender Our study methodology incorporates a mix of quantitative made by friends, they expressed a high level of overall users to interact with several recommender systems, they found the systems useful and intended to use them presented in random order. Users provided input to the gain [5]. This seemed to be due in part to the ability systems and received a set of recommendations. We the recommender systems to suggest items that users had not asked users to rate 10 recommendations from each system, previously heard of. In the words of one user, "I'm evaluating aspects such as: liking, action towards item pressed with the types of movies that came back--there (would they buy it/download it/do nothing); transparency were movies I hadn' t seen-more interesting, more obscure (if they understood why systemrecommended that item), The system pulls from a large database--no one person car and familiarity(any previous experience of the item). Users know about all the movies i might like were also asked to rate the system as a whole on a number of The results of this study offer insight into the popularity of dimensions: usefulness, trustworthiness, and ease of use. For recommender systems. While users are happy with the age Study l, we also asked users to evaluate recommendations old ways of getting recommendations, they like the breadth provided by three of their friends using similar criteria. We that online systems offer Recommender systems allow users with each system. At the end of each session, we asked recorded user behaviour and comments while they intera a unique opportunity to explore their tastes, and lean about new items users to name the system they preferred and explain their reasoning Study I involved 20 participants and Study 2 Study 1, Part 2: Interface Analysis of Book and Movie involved 12. All participants were regular Internet users, der Systems and ranged in age from 19 to 44 years. Below, we describe The next question we asked was: What constitutes a our research studies in greater detail satisfying interaction with recommender systems? To address this question, we conducted an exploratory study tudy 1, Part 1: What user needs do recommender examining the interface of three book and three movie systems satisty that a friend cannot? recommender systems Since the goal of most recommender systems is to replace Amazon. com(books and movies) (or at least augment) the social recommendation process Rating Zones Quick Picks(books) (also called word-of-mouth), we began by directly comparing the two ways of receiving recommendations Moviecritic. com (movies) (friends and online recommender systems-see Figure D) Reel. com(movies) [4]. Do users like receiving recommendations from an online A recommender system may take input from users implicitly system? How do the recommendations provided by online or explicitly, or a combination of the two [6] our stud systems differ from those provided by a user's friends? The focused on systems that relied upon explicit input. Within results of our study indicated that users preferred this subset of recommenders, we chose systems that offered recommendations made by their friends to those made by a wide variety of interaction paradigms to the user: online systems. Though users preferred recommendations differences in interfaces such as layout, navigation, color,Our study methodology incorporates a mix of quantitative and qualitative techniques. For both of our studies we asked users to interact with several recommender systems, presented in random order. Users provided input to the systems and received a set of recommendations. We then asked users to rate 10 recommendations from each system, evaluating aspects such as: liking, action towards item (would they buy it / download it / do nothing); transparency (if they understood why system recommended that item); and familiarity (any previous experience of the item). Users were also asked to rate the system as a whole on a number of dimensions: usefulness, trustworthiness, and ease of use. For Study 1, we also asked users to evaluate recommendations provided by three of their friends using similar criteria. We recorded user behaviour and comments while they interacted with each system. At the end of each session, we asked users to name the system they preferred and explain their reasoning. Study 1 involved 20 participants and Study 2 involved 12. All participants were regular Internet users, and ranged in age from 19 to 44 years. Below, we describe our research studies in greater detail. Study 1, Part 1: What user needs do recommender systems satisfy that a friend cannot? Since the goal of most recommender systems is to replace (or at least augment) the social recommendation process (also called word-of-mouth), we began by directly comparing the two ways of receiving recommendations (friends and online recommender systems—see Figure 1) [4]. Do users like receiving recommendations from an online system? How do the recommendations provided by online systems differ from those provided by a user’s friends? The results of our study indicated that users preferred recommendations made by their friends to those made by online systems. Though users preferred recommendations made by friends, they expressed a high level of overall satisfaction with the online recommenders and indicated that they found the systems useful and intended to use them again [5]. This seemed to be due in part to the ability of recommender systems to suggest items that users had not previously heard of. In the words of one user, “I’m impressed with the types of movies that came back-- there were movies I hadn't seen—more interesting, more obscure. The system pulls from a large database—no one person can know about all the movies I might like.” The results of this study offer insight into the popularity of recommender systems. While users are happy with the age - old ways of getting recommendations, they like the breadth that online systems offer. Recommender systems allow users a unique opportunity to explore their tastes, and learn about new items. Study 1, Part 2: Interface Analysis of Book and Movie Recommender Systems The next question we asked was: What constitutes a satisfying interaction with recommender systems? To address this question, we conducted an exploratory study examining the interface of three book and three movie recommender systems: · Amazon.com (books and movies) · RatingZone’s QuickPicks (books) · Sleeper (books) · Moviecritic.com (movies) · Reel.com (movies) A recommender system may take input from users implicitly or explicitly, or a combination of the two [6]; our study focused on systems that relied upon explicit input. Within this subset of recommenders, we chose systems that offered a wide variety of interaction paradigms to the user: differences in interfaces such as layout, navigation, color, Figure 1: User can choose between Online recommender systems and social recommendations (from friends) Online Recommender System Output (Recommendations) Input from user Social Recommendations