items that suit their tastes, they are not immediately willing that guides people to items they would be interested to commit any resources. In addition, MediaUnbound buying immediately, but also allows them to explore and presents only a list of individual songs, rather than complete develop their tastes in the future albums, and does not offer the means for acquiring the item (e.g. a link to an e-commerce site). Therefore the user ma ACKNOWLEDGMENTS perceive more of a psychological barrier to acquiring the We wish to thank Marti Hearst for her support of thi Finally, the user had only limited time to interact with the system during the course of our study. It is possible that in REFERENCES more realistic settings, users might have more time to 1. Konstan, J.A., Miller, B N, Maltz, D, Herlocker, J. L explore MediaUnbound's recommendations and be willing Gordon, L.R., and Riedl, J. GroupLens: Applying to commit to purchasing recommended items. Recall that all Collaborative Filtering to Usenet News. Commun. ACM of our users had indicated that they would us MediaUnbound in the future. Currently, we are following up 2. Herlocker, J, Konstan, J.A., Riedl, J. Explaining ith our study participants to find out if they have beer Collaborative Filtering recommendations. ACM 2000 using MediaUnbound as they had indicated, and whether Conference on Computer-Supported Collaborative Work they have bought any of the music recommended to them. 3. Breese, J, Heckerman, D, and Kadie, C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the 14 Conference on Uncertainty in Artificial Intelligence, 1998(43-52) Both Amazon and MediaUnbound inspired trust in the user (albeit for different reasons). However, Amazon is a 4. Resnick, P, and Varian, H.R. Recommender Systems successful model of a recommender system integrated into 1997 Commun.ACM40,3(5658) an online commerce engine. In contrast, MediaUnbound 5. Sinha, R and Swearingen, K. offers users the chance to leam more about their musical ms and friends tastes. Users liked both the systems but for different Proceedings of the DELOS-NS Our suggestion to designers is to determine the purported 6. Schafer, J B, Konstan, J.A., and Riedl, J. Recommender role of the system--its primary purpose. A system may be Systems in E-Commerce. Proceedings of the ACM designed very differently depending on the systems goals. It Conference on Electronic Commerce, November 1999 light also be possible to build some kind of a hybrid systemitems that suit their tastes, they are not immediately willing to commit any resources. In addition, MediaUnbound presents only a list of individual songs, rather than complete albums, and does not offer the means for acquiring the item (e.g. a link to an e-commerce site). Therefore the user may perceive more of a psychological barrier to acquiring the item. Finally, the user had only limited time to interact with the system during the course of our study. It is possible that in more realistic settings, users might have more time to explore MediaUnbound’s recommendations and be willing to commit to purchasing recommended items. Recall that all of our users had indicated that they would use MediaUnbound in the future. Currently, we are following up with our study participants to find out if they have been using MediaUnbound as they had indicated, and whether they have bought any of the music recommended to them. CONCLUSIONS Both Amazon and MediaUnbound inspired trust in the user (albeit for different reasons). However, Amazon is a successful model of a recommender system integrated into an online commerce engine. In contrast, MediaUnbound offers users the chance to learn more about their musical tastes. Users liked both the systems but for different purposes. Our suggestion to designers is to determine the purported role of the system—its primary purpose. A system may be designed very differently depending on the system’s goals. It might also be possible to build some kind of a hybrid system that guides people to items they would be interested in buying immediately, but also allows them to explore and develop their tastes in the future. ACKNOWLEDGMENTS We wish to thank Marti Hearst for her support of this project. REFERENCES 1. Konstan, J.A., Miller, B.N, Maltz, D., Herlocker, J.L., Gordon, L.R., and Riedl, J. GroupLens: Applying Collaborative Filtering to Usenet News. Commun. ACM 40, 3 (77-87). 2. Herlocker, J., Konstan, J.A., Riedl, J. Explaining Collaborative Filtering Recommendations. ACM 2000 Conference on Computer-Supported Collaborative Work. 3. Breese, J., Heckerman, D., and Kadie, C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998 (43-52). 4. Resnick, P, and Varian, H.R. Recommender Systems. 1997 Commun. ACM 40, 3 (56-58). 5. Sinha, R. and Swearingen, K. Comparing Recommendations made by Online Systems and Friends. Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, 2001. 6. Schafer, J.B., Konstan, J.A., and Riedl, J. Recommender Systems in E-Commerce. Proceedings of the ACM Conference on Electronic Commerce, November 1999