Interaction Design for Recommender Systems Kirsten Swearingen, Rashmi Sinha School of Information Management Systems, University of Califomia, Berkeley, CA ABSTRACT offer recommender systems as one way for consumers to find products they might like to purchase for users, aiding them in decision making about matters Typically the effectiveness of recommender systems has related to personal taste. Research has focused mostly on the en indexed by statistical accuracy metrics such as mean algorithms that drive the system, with little understanding of Absolute Error(MAE)[3]. However, satisfaction with a design issues from the user's perspective. The goal of our recommender system is only partly determined by the research is to study users' interactions with recommender accuracy of the algorithm behind it [2]. What factors lead to stems in order to develop general design guidelines. We satisfaction with a recommender system? What encourages have studied users' interactions with 1 l online recommender users to reveal their tastes to online systems, and act upon systems. Our studies have highlighted the role of the recommendations provided by such systems? While ansparency(understanding of system logic), familiar there is a lot of research on the accuracy of recommender commendations, and information about recommended system algorithms, there is little focus on interaction design items in the user's interaction with the system. Our results for recommender systems. also indicate that there are multiple models for successful To design an effective interaction, one must consider two recommender systems. questions: (I)what user needs are satisfied by interacting ith the system; and (2)what specific system features lead to satisfaction of those needs. our research studies have Evaluation, Information Retrieval, Usability Studies, User attempted to answer both of these questions. Below is a Studies, World wide Web brief overview of our study methodology and main findings Subsequently we discuss the results in greater detail and INTRODUCTION offer design guidelines based on those results. In everyday life, people must often rely on incomplete information when deciding which books to read movies to OVERMEW OF OUR RESEARCH PROGRAM watch or music to purchase. When presented with a number More than 20 different book, movie and music recommender of unfamiliar altematives, people tend to seek out systems are currently available online. Though the basic recommendations from friends or expert reviews interaction paradigm is similar(user provides some input ewspapers and magazines to aid them in decision-making and the system processes that information to generate a list In recent years, online recommender systems have begun of recommendations), recommender systems differ in the providing a technological proxy for this social specifics of the interaction(e.g, amount and type of input recommendation process. Most recommender systems work user is required to give, familiarity of recommendations, by asking users to rate some sample items. Collaborative transparency of system logic, number of recommendations) filtering algorithms, which often fom the backbone of such Our approach has been to sample a variety of interaction systems, use this input to match the current user with others models in order to identify best practices and generate who share similar tastes. Recommender systems have inners of Recommender Systems. For 6 of gained increasing popularity on the web, both in research the ll systems tested, we also compared user's liking for ystems(e.g GroupLens [1] and MovieLens [2) and online systems recommendations with liking for recommendations commercesites(e.g.AmazoncomandCdnow.com),th provided by their friends. Permission to make digal or hard copies of al a part d this work for arena made or distributed or proft or commercal advantage and that otherwise, ar republish, to post on servers or o redistribute to ists, DIS2002, London@ Copyright 2002 ACM1-58113-29-000000B $5.00 berkeley. edu, sinhala rkeley.eduInteraction Design for Recommender Systems Kirsten Swearingen, Rashmi Sinha School of Information Management & Systems, University of California, Berkeley, CA1 1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems. Keywords Evaluation, Information Retrieval, Usability Studies, User Studies, World Wide Web INTRODUCTION In everyday life, people must often rely on incomplete information when deciding which books to read, movies to watch or music to purchase. When presented with a number of unfamiliar alternatives, people tend to seek out recommendations from friends or expert reviews in newspapers and magazines to aid them in decision-making. In recent years, online recommender systems have begun providing a technological proxy for this social recommendation process. Most recommender systems work by asking users to rate some sample items. Collaborative filtering algorithms, which often form the backbone of such systems, use this input to match the current user with others who share similar tastes. Recommender systems have gained increasing popularity on the web, both in research systems (e.g. GroupLens [1] and MovieLens [2]) and online commerce sites (e.g. Amazon.com and CDNow.com), that offer recommender systems as one way for consumers to find products they might like to purchase. Typically the effectiveness of recommender systems has been indexed by statistical accuracy metrics such as Mean Absolute Error (MAE) [3]. However, satisfaction with a recommender system is only partly determined by the accuracy of the algorithm behind it [2]. What factors lead to satisfaction with a recommender system? What encourages users to reveal their tastes to online systems, and act upon the recommendations provided by such systems? While there is a lot of research on the accuracy of recommender system algorithms, there is little focus on interaction design for recommender systems. To design an effective interaction, one must consider two questions: (1) what user needs are satisfied by interacting with the system; and (2) what specific system features lead to satisfaction of those needs. Our research studies have attempted to answer both of these questions. Below is a brief overview of our study methodology and main findings. Subsequently we discuss the results in greater detail and offer design guidelines based on those results. OVERVIEW OF OUR RESEARCH PROGRAM More than 20 different book, movie and music recommender systems are currently available online. Though the basic interaction paradigm is similar (user provides some input and the system processes that information to generate a list of recommendations), recommender systems differ in the specifics of the interaction (e.g., amount and type of input user is required to give, familiarity of recommendations, transparency of system logic, number of recommendations). Our approach has been to sample a variety of interaction models in order to identify best practices and generate guidelines for designers of Recommender Systems. For 6 of the 11 systems tested, we also compared user’s liking for systems’ recommendations with liking for recommendations provided by their friends. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires specific permission and/or a fee. DIS2002, London © Copyright 2002 ACM 1-58113-2-9-0/00/0008 $5.00