other points in the network. It might thus be worth consid- cantly expand impact and importance of recommender ering hybrid approaches in which some preliminary inter systems in a networked world pretation is performed locally when the observaton is made and then additional inferences are drawn at other points References the network Brin, S and Page, L. 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Dept. of Computer Sci- ence, Stanford Uniy Inference ↓mmg Goldberg, D, Nichols, D, Oki, B M, and Terry, D. 1992. Using Collaborative Filtering to Weave an Information Prediction Tapestry. Communication of the ACM, December, 35(12) Predicted ratings Garfield, E. 1979. Citation Indexing: Its Theory and Ap- plication in Science, Technology, and Hum York: Wiley-Interscience Figure 1. Rating estimation strategy Hill, w.C. hollan, D. wrobelwski, D and McCandless T. 1992. Read Wear and Edit Wear. In: Proceedings of Observations ACM Conference on Human Factors in Computing Sys- Prediction Karlgren, J. 1994. Newsgroup Clustering Based on User Predicted observations Behavior: A Recommendation Algebra. Tecl Research Reports from SICS, T94-01 Inference htp/wwsicsse/babstracshtml#T94/04. Konstan, J. A, Miller, B. N, Maltz, D, Herlocker, J. L Predicted rating Gordon, L R, and Riedl, J. 1997. GroupLens: Applying Collaborative Filtering to Usenet News Communications of the ACM, March, 40(3): 77-87 Figure 2. Predicted observations strategy. Morita, M. and Shinoda, Y. 1994. Information Filtering Conclusion Based on User Behavior Analysis and Best Match Text Retrieval. In Proceedings of the Seventeenth Annual Inter- We have presented three potential sources for implicit national ACM-SIGIR Conference on Research and Devel feedback and described two ways those sources could be opment in Information Retrieval, 272-281 used by recommender systems. Our "examination"cate- gory seeks to capture ephemeral interactions that begin and Nichols, D. M. 1997. Implicit Ratings and filtering. In groups user behaviors that suggest an intention for future Collaborative Filtering, 10-12. Budapaest, Hungary, se of the material. Our third category is reference, which ERCIM includes user behaviors that create explicit or explicit links between information objects. We believe these categories Oard, D. w. 1997. The State of the Art in Text Filtering group observable behavior in a way that is useful when User Modeling and User-Adapted Interaction, 7(3): 141 thinkingabouthowtomakepredictionsandtowardthat178.http:/lwwwglueumdedu/oard/researchhtml end we have suggested two strategies for using implicit feedback in recommender systems. Our present work is Rucker, J and Polanco, M.J. 1997. Personalized Naviga- focused on understanding how to relate observations to tion for the Web Communications of the ACM, March, predicted ratings, both individually and in various combi- 40(3):73-89 nations that could be more informative than single-source observations. We then hope to develop and implement a Stevens, C. 1993. Knowledge-Based Assistance for Ac prototype that will give us some insight into how implicit cessing Large, Poorly Structured Information Spaces Feedback can be used effectively in an application envi- Ph D dissertation, Dept of Computer Science, Univ of ronment. If successful, this approach could help transcend Colorado. Boulder the current reliance on explicit ratings and thus signifiother points in the network. It might thus be worth consid￾ering hybrid approaches in which some preliminary inter￾pretation is performed locally when the observaton is made and then additional inferences are drawn at other points in the network. Observations Inference I Estimated ratings Prediction I Predicted ratings Figure 1. Rating estimation strategy. Observations Prediction I Predicted observations Inference I Predicted ratings Figure 2. Predicted observations strategy. Conclusion We have presented three potential sources for implicit feedback and described two ways those sources could be used by recommender systems. Our "examination" cate￾gory seeks to capture ephemeral interactions that begin and end during a single session, while the "retention" category groups user behaviors that suggest an intention for future use of the material. Our third category is reference, which includes user behaviors that create explicit or explicit links between information objects. We believe these categories group observable behavior in a way that is useful when thinking about how to make predictions, and toward that end we have suggested two strategies for using implicit feedback in recommender systems. Our present work is focused on understanding how to relate observations to predicted ratings, both individually and in various combi￾nations that could be more informative than single-source observations. We then hope to develop and implement a prototype that will give us some insight into how implicit feedback can be used effectively in an application envi￾ronment. If successful, this approach could help transcend the current reliance on explicit ratings and thus signifi￾cantly expand impact and importance of recommender systems in a networked world. References Brin, S. and Page, L. 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Dept. of Computer Sci￾ence, Stanford Univ. http_ ://google.stanford.edu/~backrub/google.html. Goldberg, D., Nichols, D., Oki, B. M, and Terry, D. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Communication of the ACM, December, 35 (12 ): 61-70. Garfield, E. 1979. Citation Indexing: Its Theory and Ap￾plication in Science, Technology, and Humanities. New York: Wiley-Interscience. Hill, W.C., Hollan, J. D., Wrobelwski, D. and McCandless, T. 1992. Read Wear and Edit Wear. In: Proceedings of ACM Conference on Human Factors in Computing Sys￾tems, CHI ’92: 3-9. Karlgren, J. 1994. Newsgroup Clustering Based on User Behavior: A Recommendation Algebra. Technical and Research Reports from SICS, T94-01. http_ ://www. ~ic s. se/libabstracts.html#T94/04. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM, March, 40(3): 77-87. Morita, M. and Shinoda, Y. 1994. Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval. In Proceedings of the Seventeenth Annual Inter￾national ACM-SIGIR Conference on Research and Devel￾opment in Information Retrieval, 272-281. Nichols, D. M. 1997. Implicit Ratings and Riltering. In Proceedings of the 5 th DELOS Workshop on Filtering and Collaborative Filtering, 10-12. Budapaest, Hungary, ERCIM. Oard, D. W. 1997. The State of the Art in Text Filtering. User Modeling and User-Adapted Interaction, 7(3): 141- 178. http;//www.glue.umd.edu/-oard/research.html. Rucker, J. and Polanco, M. J. 1997. Personalized Naviga￾tion for the Web. Communications of the ACM, March, 40(3): 73-89. Stevens, C. 1993. Knowledge-Based Assistance for Ac￾cessing Large, Poorly Structured Information Spaces. Ph.D. dissertation, Dept. of Computer Science, Univ. of Colorado, Boulder. http;//www.holodeck.com/curt/m_vp, apers.html. 83