Desenvolvimento de um Modelo Semântico para Recomendação Baseado em Grafos
Resumo
Os sistemas de recomendação têm como objetivo auxiliar um usuário, ou grupos de usuários, na identificação de diferentes itens relevantes às suas necessidades. Com a crescente quantidade de dados, os sistemas de recomendação estão sendo muito estudados, pois é cada vez mais difícil encontrar a informação desejada devido à grande quantidade de alternativas disponíveis. Neste trabalho é proposto e desenvolvido um modelo para um sistema de recomendações baseado em grafos bipartidos, composto por informações semânticas de usuários e itens. Este modelo de recomendação foi avaliado com base em duas provas de conceitos (PoC).
Palavras-chave:
Sistema de Recomendação, Informações Semânticas, Grafos Bipartidos
Referências
Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734–749.
Baeza-Yates, R., Ribeiro-Neto, B., et al. (1999). Modern information retrieval, volume 463. ACM press New York.
Balabanovíc, M. and Shoham, Y. (1997). Fab: contentbased, collaborative recommendation. Communications of the ACM, 40(3):66–72.
Basili, V. R. (1992). Software modeling and measurement: the goal/question/metric paradigm.
Goldenberg, J., Libai, B., Muller, E., and Stremersch, S. (2010). Database submission-the evolving social network of marketing scholars. Marketing Science, 29(3):561–567.
Han, J. and Kamber, M. (2006). Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan kaufmann.
Kunegis, J., De Luca, E.W., and Albayrak, S. (2010). The link prediction problem in bipartite networks. In Computational intelligence for knowledge-based systems design, pages 380–389. Springer.
Maehara, C., Yatsugi, K., Kim, D., and Ushiama, T. (2012). An exhibit recommendation system based on semantic networks for museum. In Innovations in Intelligent Machines–2, pages 131–141. Springer.
Oestreicher-Singer, G. and Sundararajan, A. (2010). Recommendation networks and the long tail of electronic commerce. Available at SSRN 1324064.
Saif, H., He, Y., and Alani, H. (2012). Semantic sentiment analysis of twitter. In The Semantic Web–ISWC 2012, pages 508–524. Springer.
Sawant, S. (2013). Collaborative filtering using weighted bipartite graph projection: a recommendation system for yelp. CS224W: Social and Information Network Analysis (December 10, 2013).
Shi, Y., Larson, M., and Hanjalic, A. (2014). Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1):3.
Shieh, J.-R., Yeh, Y.-T., Lin, C.-H., Lin, C.-Y., and Wu, J.-L. (2008). Collaborative knowledge semantic graph image search. In Proceedings of the 17th international conference on World Wide Web, pages 1055–1056. ACM.
Ströele, V., Zimbrão, G., and Souza, J. M. (2013). Group and link analysis of multi-relational scientific social networks. The Journal of Systems and Software, 86:1819–1830.
Ting, Y., Yan, C., and Xiang-wei, M. (2013). Personalized recommendation system based on web log mining and weighted bipartite graph. In Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on, pages 587–590. IEEE.
Wasserman, S. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.
Baeza-Yates, R., Ribeiro-Neto, B., et al. (1999). Modern information retrieval, volume 463. ACM press New York.
Balabanovíc, M. and Shoham, Y. (1997). Fab: contentbased, collaborative recommendation. Communications of the ACM, 40(3):66–72.
Basili, V. R. (1992). Software modeling and measurement: the goal/question/metric paradigm.
Goldenberg, J., Libai, B., Muller, E., and Stremersch, S. (2010). Database submission-the evolving social network of marketing scholars. Marketing Science, 29(3):561–567.
Han, J. and Kamber, M. (2006). Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan kaufmann.
Kunegis, J., De Luca, E.W., and Albayrak, S. (2010). The link prediction problem in bipartite networks. In Computational intelligence for knowledge-based systems design, pages 380–389. Springer.
Maehara, C., Yatsugi, K., Kim, D., and Ushiama, T. (2012). An exhibit recommendation system based on semantic networks for museum. In Innovations in Intelligent Machines–2, pages 131–141. Springer.
Oestreicher-Singer, G. and Sundararajan, A. (2010). Recommendation networks and the long tail of electronic commerce. Available at SSRN 1324064.
Saif, H., He, Y., and Alani, H. (2012). Semantic sentiment analysis of twitter. In The Semantic Web–ISWC 2012, pages 508–524. Springer.
Sawant, S. (2013). Collaborative filtering using weighted bipartite graph projection: a recommendation system for yelp. CS224W: Social and Information Network Analysis (December 10, 2013).
Shi, Y., Larson, M., and Hanjalic, A. (2014). Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1):3.
Shieh, J.-R., Yeh, Y.-T., Lin, C.-H., Lin, C.-Y., and Wu, J.-L. (2008). Collaborative knowledge semantic graph image search. In Proceedings of the 17th international conference on World Wide Web, pages 1055–1056. ACM.
Ströele, V., Zimbrão, G., and Souza, J. M. (2013). Group and link analysis of multi-relational scientific social networks. The Journal of Systems and Software, 86:1819–1830.
Ting, Y., Yan, C., and Xiang-wei, M. (2013). Personalized recommendation system based on web log mining and weighted bipartite graph. In Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on, pages 587–590. IEEE.
Wasserman, S. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.
Publicado
05/07/2016
Como Citar
FERNANDES, Hugo Guércio; STRÖELE, Victor.
Desenvolvimento de um Modelo Semântico para Recomendação Baseado em Grafos. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 2016. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 103-114.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2016.6447.
