Abstract
Collaborative Filtering and Content-Based Filtering are techniques used in the design of Recommender Systems that support personalization. Information that is available about the user, along with information about the collection of users on the system, can be processed in a number of ways in order to extract useful recommendations. There have been several algorithms developed, some of which we briefly introduce, which attempt to improve performance by maximizing the accuracy of their predictions. We describe a novel algorithm in which user models are represented as Conceptual Graphs and report on results obtained using the EachMovie dataset. We compare the algorithms based on the average error of prediction and standard deviation and discuss our method’s strengths and advantages.
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References
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)
Wahlster, W., Kobsa, A.: User Models in Dialog Systems. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, Springer, Heidelberg (1989)
Kobsa, A., Koenemann, J., Pohl, W.: Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16(2), 111–155 (2001)
Chen, Q., Norcio, A.F.: Modeling a user’s domain knowledge with neural networks. International Journal of Human-Computer Interaction 9(1), 25–40 (1997)
Jennings, A., Higuchi, H.: A user model neural network for a personal news service. User Modeling and User Adapted Interaction 3, 1–25 (1993)
Lang, K.: NewsWeeder: Learning to filter Netnews. In: Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA (1995)
Billsus, D., Pazzani, M.J.: A Hybrid User Model for News Story Classification. In: Kay, J. (ed.) User Modeling 1999 - Proceedings of the 7th International Conference, pp. 99–108. Springer, Wien-New York (1999)
Sheth, B., Maes, P.: Evolving agents for personalized information filtering. In: Proceedings of the Ninth Conference on AI for Applications, IEEE Computer Society Press, Los Alamitos (1993)
Balabanovic, M., Shoham, Y.: Fab: Content-based collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)
Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313–331 (1997)
Oard, D.W., Kim, J.: Implicit feedback for Recommender Systems. In: Proceedings of the AAAI Workshop on Recommender Systems, Madison, WI, pp. 81–83 (1998)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of Netnews. In: Proceedings of ACM Conference on Computer-Supported Cooperative Work, pp. 175–186 (1994)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, WI, pp. 43–52 (1998)
Ha, V., Haddawy, P.: Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures. In: Proceedings of 14th Conference on Uncertainty in Artificial Intelligence, Madison, WI, pp. 193–201 (1998)
Pennock, D.M., Horvitz, E., Lawrence, S., Lee Giles, C.: Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 473–480 (2000)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word of mouth. In: Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 210–217 (1995)
Wu, H., Salton, G.: A Comparison of Search Term Weighting: Term Relevance vs. Inverse Document Frequency. In: Proceedings of the 4th International Conference on Information Storage and Retrieval: Theoretical Issues in Information Retrieval (1981)
Alspector, J., Kolcz, A., Karunanithi, N.: Comparing Feature-Based and Clique-Based User Models for Movie Selection. In: Proceedings of the 3rd ACM Conference on Digital Libraries, Pittsburgh, PA (1998)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: Applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 77–87 (1997)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Proceedings of the 16th National Conference on Artificial Intelligence (1999)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International World Wide Web Conference, Hong Kong (2001)
Claypool, M., Gokhale, A., Miranda, T.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems (1999)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In: Proceedings of the 15th National Conference on Artificial Intelligence (1998)
Sowa, J.F.: Conceptual Structures. Addison-Wesley, Reading (1984)
Tzanavari, A., Paulson, P.: Intelligent Information Processing for User Modeling. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annecy, France, pp. 1763–1770 (2002)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
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Paulson, P., Tzanavari, A. (2003). Combining Collaborative and Content-Based Filtering Using Conceptual Graphs. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_9
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DOI: https://doi.org/10.1007/978-3-540-39906-3_9
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