Um Método de Filtragem Híbrida Baseado em Perfis Simbólicos Colaborativos
Resumo
Os Sistemas de Recomendação têm se tornado uma ferramenta importante para lidar com o problema de sobrecarga de informação a partir da aquisição do perfil do usuário. Neste artigo nós descrevemos uma abordagem para melhorar a qualidade das recomendações nas primeiras interações do usuário com o sistema. A idéia básica é: (1) primeiramente, nós descrevemos os itens a partir das opiniões de todos os usuários sobre estes; e (2) após isso, utilizamos estruturas simbólicas modais para armazenar o conteúdo do perfil. O método proposto supera, segundo a métrica half-life utility sobre a tarefa Find Good Items, outras abordagens baseadas nas técnicas: Filtragem Cognitiva, Filtragem Social e outros métodos híbridos.Referências
Adomavicius G., Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art. Knowledge and Data Engineering, IEEE, 2005.
Balanovic, M. and Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM, Vol. 40 (1997) 88-89.
Bezerra, B. L. D. and De Carvalho, F. de A. T.. A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. In: Australian Joint Conference on Artificial Intelligence AI2004, Cairns. Proceedings of the 17th Australian Joint Conference on Artificial Intelligence. Berlin (Alemanha): Springer-Verlag, 2004.
Bock, H.H. and Diday, E.: Analysis of Symbolic Data. Springer, Heidelberg (2000).
Breese, J., Heckerman, D., and Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (1998) 43-52.
Burke, R.. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction. November, 2002.
Claypool, M., Brown, D., Phong Le, Waseda M. Inferring User Interests. IEEE Internet Computing, Vol. 5 (2001), 32-39.
De Carvalho, F.A.T. and Bezerra, B.L.D.: Information Filtering based on Modal Symbolic Objects. Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation (GfKl), Springer (2002) 395-404.
Hasenjäger, M. Active Data Selection in Supervised and Unsupervised Learning. PhD thesis, Technische Fakultät der Universität Bielefeld (2000).
Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, Vol. 22, Issue 1 (2004) 5-53.
Melville, P., Mooney, R.J., and Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence (2002) 187-192.
Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, Vol. 13 (5-6), (1999) 393-408.
Popescul, A., Ungar, L.H., Pennock, D.M., and Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. 17th Conference on Uncertainty in Artificial Intelligence (2001).
Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B. and Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (1998) 345-354.
Schafer, J.B., Konstan, J.A., and Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, Vol. 5. (2001) 115-153.
Witten, I.H. and Frank, E.: Data Mining Practical Machine Learning Tools and Techniques with Java Implementations. San Diego, CA: Morgan Kaufmann, (2000).
Yu, K., Schwaighofer, A., Tresp, V., Ma, W.-Y., and Zhang, H.: Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical Bayes. In C. Meek and U. Kjærulff, editors, Proceedings of UAI 2003, Morgan Kaufmann, (2003) 616-623.
Balanovic, M. and Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM, Vol. 40 (1997) 88-89.
Bezerra, B. L. D. and De Carvalho, F. de A. T.. A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. In: Australian Joint Conference on Artificial Intelligence AI2004, Cairns. Proceedings of the 17th Australian Joint Conference on Artificial Intelligence. Berlin (Alemanha): Springer-Verlag, 2004.
Bock, H.H. and Diday, E.: Analysis of Symbolic Data. Springer, Heidelberg (2000).
Breese, J., Heckerman, D., and Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (1998) 43-52.
Burke, R.. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction. November, 2002.
Claypool, M., Brown, D., Phong Le, Waseda M. Inferring User Interests. IEEE Internet Computing, Vol. 5 (2001), 32-39.
De Carvalho, F.A.T. and Bezerra, B.L.D.: Information Filtering based on Modal Symbolic Objects. Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation (GfKl), Springer (2002) 395-404.
Hasenjäger, M. Active Data Selection in Supervised and Unsupervised Learning. PhD thesis, Technische Fakultät der Universität Bielefeld (2000).
Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, Vol. 22, Issue 1 (2004) 5-53.
Melville, P., Mooney, R.J., and Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence (2002) 187-192.
Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, Vol. 13 (5-6), (1999) 393-408.
Popescul, A., Ungar, L.H., Pennock, D.M., and Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. 17th Conference on Uncertainty in Artificial Intelligence (2001).
Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B. and Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (1998) 345-354.
Schafer, J.B., Konstan, J.A., and Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, Vol. 5. (2001) 115-153.
Witten, I.H. and Frank, E.: Data Mining Practical Machine Learning Tools and Techniques with Java Implementations. San Diego, CA: Morgan Kaufmann, (2000).
Yu, K., Schwaighofer, A., Tresp, V., Ma, W.-Y., and Zhang, H.: Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical Bayes. In C. Meek and U. Kjærulff, editors, Proceedings of UAI 2003, Morgan Kaufmann, (2003) 616-623.
Publicado
30/06/2007
Como Citar
BEZERRA, Byron Leite Dantas; CARVALHO, Francisco de Assis Tenório; MACÁRIO FILHO, Valmir.
Um Método de Filtragem Híbrida Baseado em Perfis Simbólicos Colaborativos. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 1282-1291.
ISSN 2763-9061.
