Amazon 600K QUCK PICKS Rating Zor 心 ommendation Halp us improve your recommendations h two easy stops. Enter your favorite interests, products, authors, or at Athar Faverite Artist Japo rrn (Jhn5b,phn构v》(Th图时呢影。协 Favorite Movie Fevorite Interes Figure 2: Interaction Paradigms for Amazon(Books )& Rating Zone graphics, and user instructions, types of input required, and Song Explorer information displayed with recommendations(see Figure 2 Media Unbound(5-minute version for illustration, and Appendixfor full system comparison From this study, we found that trust was affected by several aspects of the users' interactions with the systems, in Our findings in this study suggested that, from a user's ddition to the accuracy of the recommendations themselves perspective, an effective recommender system inspires trus ransparency of system logic, familiarity of the items in the system; has system logic that is at least somewhat recommended, and the process for receiving transparent; points users towards new, not-yet-experienced recommendations items; provides details about recommended items, including res and community ratings, and finally, provides Interaction Design for Recommender Systems to refine recommendations by including or excluding divided into thre particular genres. Users expressed willingness to provide parts. User interaction with such systems typically involves more input to the system in retum for more effective some input to the system; the system processes this input: and the user receives the output or recommendations. First ve take recommender systems apart and analyse the input Study 2: Interface Analysis of Music and output phases. What characteristics of these two phases Recommender Systems distinguish recommender systems? Which of these design The goal of our second study was to verify the findings from options do users prefer and why Study I, and extend them to another recommendation User interaction with recommender systems can also be domainthat of music. In Study I we had focused on conceptualised on a more gestalt or holistic level. What specific aspects of the interface(number of input items, overall system features lead to satisfaction with number of results etc. ) In Study 2 we considered the recommendations? How do users decide whether to trust systems more holistically, seeking in particular to answer the recommendations? What kinds of recommendations do they question"what leads a user to trust the systems find the most useful? For each of these questions, we recommendations? describe pertinent study results(both quantitative and In this study, qualitative), and suggest design options systems, for two reasons. First, with the increasing availability and usage of online music, we anticipate that 1)TAKING THINGS APART: INPUT TO THE SYSTEM music recommender systems will increase in popularity Recommender systems differ widely in terms of the type and Second, and more importantly, music recommenders allow amount of input users must provide in order to generate users to sample the item recommended--most systems recommendations. Some recommender systems use an open- provide access to a 30 second audio sample. This gave us ended technique, asking users to indicate their favorite the unique opportunity to evaluate the efficacy of author, musician, or actor. Other systems ask users to rate a commendations in the lab setting. Users could sample the eries of given items(books, songs, or movies)on a Likert audio clip during the test session. Thus, their evaluations of Scale, while still others use a hybrid technique first asking the recommended items are based upon direct experience general questions about taste(e.g, what phrase best rather than an abstract estimate of liking indicates how you feel about FM radio? followed by ratings We examined five music recommender systems of individual items, followed by item comparisons(e.g. de Amazons Recommendations explorer you like this song m ore or less than this other song?) CDNow Mood Logic Filters browsergraphics, and user instructions, types of input required, and information displayed with recommendations (see Figure 2 for illustration, and Appendixfor full system comparison chart). Our findings in this study suggested that, from a user’s perspective, an effective recommender system inspires trust in the system; has system logic that is at least somewhat transparent; points users towards new, not-yet-experienced items; provides details about recommended items, including pictures and community ratings; and finally, provides ways to refine recommendations by including or excluding particular genres. Users expressed willingness to provide more input to the system in return for more effective recommendations. Study 2: Interface Analysis of Music Recommender Systems The goal of our second study was to verify the findings from Study 1, and extend them to another recommendation domain—that of music. In Study 1 we had focused on specific aspects of the interface (number of input items, number of results etc.). In Study 2 we considered the systems more holistically, seeking in particular to answer the question “what leads a user to trust the system’s recommendations?” In this study, we chose to examine music recommender systems, for two reasons. First, with the increasing availability and usage of online music, we anticipate that music recommender systems will increase in popularity. Second, and more importantly, music recommenders allow users to sample the item recommended—most systems provide access to a 30 second audio sample. This gave us the unique opportunity to evaluate the efficacy of recommendations in the lab setting. Users could sample the audio clip during the test session. Thus, their evaluations of the recommended items are based upon direct experience rather than an abstract estimate of liking. We examined five music recommender systems: · Amazon’s Recommendations Explorer · CDNow · Mood Logic Filters Browser · Song Explorer · Media Unbound (5-minute version) From this study, we found that trust was affected by several aspects of the users’ interactions with the systems, in addition to the accuracy of the recommendations themselves: transparency of system logic, familiarity of the items recommended, and the process for receiving recommendations. Interaction Design for Recommender Systems Our analysis of recommender systems is divided into three parts. User interaction with such systems typically involves some input to the system; the system processes this input; and the user receives the output or recommendations. First we take recommender systems apart and analyse the input and output phases. What characteristics of these two phases distinguish recommender systems? Which of these design options do users prefer and why? User interaction with recommender systems can also be conceptualised on a more gestalt or holistic level. What overall system features lead to satisfaction with recommendations? How do users decide whether to trust recommendations? What kinds of recommendations do they find the most useful? For each of these questions, we describe pertinent study results (both quantitative and qualitative); and suggest design options. 1) TAKING THINGS APART: INPUT TO THE SYSTEM Recommender systems differ widely in terms of the type and amount of input users must provide in order to generate recommendations. Some recommender systems use an open￾ended technique, asking users to indicate their favorite author, musician, or actor. Other systems ask users to rate a series of given items (books, songs, or movies) on a Likert Scale, while still others use a hybrid technique first asking general questions about taste (e.g., what phrase best indicates how you feel about FM radio?) followed by ratings of individual items, followed by item comparisons (e.g. do you like this song more or less than this other song?). Figure 2: Interaction Paradigms for Amazon (Books) & RatingZone Amazon RatingZone