Abstract
The aim in information filtering is to provide users with a personalised selection of information, based on their interest profile. In adaptive information filtering, this profile partially or completely acquired by automatic means. This paper investigates if profile generation can be partially acquired by automatic methods and partially by direct user involvement. The issue is explored through an empirical study of a simulated filtering system that mixes automatic and manual profile generation. The study covers several issues involved in mixed control. The first issue concerns if a machine-learned profile can provide better filtering performance if generated from an initial explicit user profile. The second issue concerns if user involvement can improve on a system-generated or adapted profile. Finally, the relationship between filtering performance and user ratings is investigated. In this particular study the initial setup of a personal profile was effective and yielded performance improvements that persisted after substantiate training. However, the study showed no correlation between users’ ratings of profiles and profile filtering performance, and only weak indications that users could improve profiles that already had been trained on feedback.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Balabanovíc, M. and Shoham, Y.: 1997, Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66–72.
Baudisch, P.: 1999, Joining Collaborative and Content-based Filtering CHI ‘99 Workshop on Interacting with Recommender Systems, online proceedings. Available from http://ipsi.fhg.de/~baudisch/publications/1999-Baudisch-RecSys99-joiningcollaborativeandcontent-basedfiltering.pdf.
Baudish, P.: 1997, The Profile editor: Designing a Direct Manipulative Tool for Assembling Profiles. 5th DELOS Workshop on Filtering and Collaborative Filtering, Budapest, pp. 11–17. Available from http://citeseer.nj.nec.com/baudisch97profile.html.
Breese, J., Heckerman, D. and Kadie, C.: 1998, Empirical Analysis of Predictive Algorithms for Collaborative Filtering 14th Conference on Uncertainty in Artificial Intelligence, San Francisco, Morgan Kaufmann, pp. 43–52.
Bollacker, K., Lawrence, S. and Giles, C. L.: 1998, CiteSeer: An Autonomous Web Agent for Automatic Retrieval and Identification of Interesting Publications Second International Conference on Autonomous Agents, ACM Press, New York, pp. 116–123.
Brin, S. and Page, L.: 1998, The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1-7), 107–117.
Brusilovsky, P., Schwarz, E. and Weber, G.: 1996, A Tool for Developing Adaptive Electronic Textbooks on WWW WebNet’96-World Conference of the Web Society, Boston, MA, pp. 64–69.
Burke, R.: 1999, Integrating Knowledge-based and Collaborative-filtering Recommender Systems. Workshop on AI and Electronic Commerce, AAAI’99, Orlando, Florida.Available from http://citeseer.nj.nec.com/burke99integrating.html.
Burke R., Hammond, K. and Young, B.: 1997, The FINDME approach to assisted browsing. IEEE Expert, 12(4), 32–40.
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M.: 1999, Combining Content-based and Collaborative Filters in an Online Newspaper. ACM SIGIR Workshop on Recommender Systems. Available from http://citeseer.nj.nec.com/claypool99combining.html.
Claypool, M., Brown, D. and Phong, M.: 2001, Inferring user interests. IEEE Internet Computing, 5(6), 32–39.
Diao, Y., Lu, H. and Wu, D.: 2000, A Comparative Study of Classification-based Personal e-mail Filtering, 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kyoto, JP, Springer Verlag, Heidelberg, DE, pp. 408–419.
Grasso, A., Meunier, J-L. and Thompson, C.: 2000, Augmenting Recommender Systems by Embedding Interfaces into Practices 33rd Hawaii International Conference on System Sciences-Volume 3, Maui, Hawaii, pp. 3016. Available from http://citeseer.nj.nec.com/grasso99augmenting.html.
Herlocker, J., Konstan, J., Borchers, A. and Riedl, J.: 1999, An Algorithmic Framework for Performing Collaborative Filtering The 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, California, United States, pp. 230–237.
Herlocker, J., Konstan, J. and Riedl, J.: 2000, Explaining Collaborative Filtering Recommendations ACM 2000 Conference on Computer Supported Cooperative Work, December 2000, Philadelphia, Penn. pp. 241–250.
Hofmann, T. and Puzicha, J.: 1999, Latent Class Models for Collaborative Filtering International Joint Conference on Artificial Intelligence, Stockholm, Morgan Kaufmann Publishers, pp. 688–693.
Karlgren, J., Höök, K., Lantz, A., Palme, J. and Pargman, D.: 1994, The Glass Box User Model for Filtering Report R94014, Departments of Computer and Systems Sciences, Computational Linguistics, and Psychology, Stockholm University. Available from http://citeseer.nj.nec.com/karlgren94glass.html.
Kim, J., Oard, D. and Romanik, K.: 2000, Using Implicit Feedback for User Modeling in Internet and Intranet Searching Technical Report, College of Library and Information Services, University of Maryland at College Park. Available from http://citeseer.nj.nec.com/kim00using.html.
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. and Riedl, J.: 1997, GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3),77–87.
Kohrs, A. and Merialdo, B.: 2000, Using Category-based Collaborative Filtering in the Active Web Museum IEEE International Conference on Multimedia and Expo, New York,pp. 351–354.
Kolodner, J.: 1993, Case-Based Reasoning, Morgan Kaufmann Publishers, San Mateo, California.
Macskassy, S., Hirsh, H. and Dayanik, A.: 1999, EmailValet: Learning Email Preferences for Wireless Platforms IJCAI workshop on Learning About Users, Stockholm. Available from http://citeseer.nj.nec.com/article/macskassy99emailvalet.html.
Mobasher, B., Dai, H. and Tao, M.: 2002, Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6, 61–82.
Munro, A., Höök, K. and Benyon, D. (eds.) 1999, Social Navigation in Information Space, Springer Verlag, London.
Pazzani, M.J.: 2000a, Information Filtering, Classification, and Extraction Tutorial notes, Intelligent User Interfaces Conference, New Orleans, Louisiana.
Pazzani, M. J.: 2000b, Representation of Electronic Mail Filtering Profiles: A User Study Intelligent User Interfaces Conference, New Orleans, Louisiana, pp. 202–206.
Popescul, A., Ungar, L., Pennock, D. and Lawrence, S.: 2001, Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments 17th Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, pp. 437–444.
Rocchio, J.: 1971, Relevance Feedback Information Retrieval. In: G. Salton, (ed.) The Smart Retrieval System, Experiments in Automatic Document Processing, Prentice-Hall, Englewood Cliffs, NJ, pp. 313–323.
Sarwar, B., Konstan, J., Borchersm, A., Herlocker, J., Miller, B. and Riedl, J.: 1998, Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System ACM Conference on Computer Supported Cooperative Work, pp. 345–354.
Schafer, B., Konstan, J. and Riedl, J.: 2001, E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2), 115–153.
Terveen, L., Hill, W., Amento, B., McDonald, D. and Creter, J.: 1998, PHOAKS: A system for sharing recommendations. Communications of the ACM, 40(3), 59–62.
Ungar, L. H. and Foster, D. P.: 1998, Clustering Methods for Collaborative Filtering In:AAAI Workshop on Recommendation Systems, Madison, Wisconsin. Available from http://citeseer.nj.nec.com/ungar98clustering.html.
Waern, A., Tierney, M., Rudström, Å. and Laaksolahti, J.: 1998, ConCall: An Information Service for Researchers Based on EdInfo SICS technical report T98:04. Available as http://www.sics.se/mark/papers/t9804/t98-04.htm.
Waern, A., Tierney, M., Rudström, Å Laaksolahti, J. and Mård, T.: 1999, ConCall: Edited and Adaptive Information Filtering International Conference on Intelligent User Interfaces, Los Angeles, California, ACM, pp. 185.
Wasfi, A.: 1999, Collecting User Access Patterns for Building User Profiles and Collaborative Filtering International Conference on Intelligent User Interfaces, Los Angeles, California, ACM, pp. 57–64.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Wærn, A. User Involvement in Automatic Filtering: An Experimental Study. User Model User-Adap Inter 14, 201–237 (2004). https://doi.org/10.1023/B:USER.0000028984.13876.9b
Issue Date:
DOI: https://doi.org/10.1023/B:USER.0000028984.13876.9b