Aprendizado de Máquina em Sistemas de Recomendação Baseados em Conteúdo Textual: Uma Revisão Sistemática
Resumo
Sistemas de Recomendação baseados em Conteúdo (SRbC) é uma área em que estratégias de Aprendizado de Máquina (AM) podem ser potencialmente aplicadas com êxito. Contudo, especificamente na área de SRbC textual, o uso de AM não tem sido expressivo nos últimos anos. Neste artigo é apresentada uma Revisão Sistemática para identificação, interpretação e avaliação de como estratégias de AM vêm sendo utilizadas no contexto de SRbC textual a fim de contribuir para a evolução da interseção de tais áreas.
Palavras-chave:
Sistemas de Recomendação baseado em Conteúdo, Conteúdo Textual, Aprendizado de Máquina, Revisão Sistemática
Referências
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Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. 42(8):30–37, Aug. 2009.
Y. Wang, J. Liu, X. Dong, T. Liu, and Y. Huang. Personalized paper recommendation based on user historical behavior. In M. Zhou, G. Zhou, D. Zhao, Q. Liu, and L. Zou, editors, Natural Language Processing and Chinese Comp., volume 333 of Commun. in Comp. and Inf. Sci., pages 1–12. Springer, 2012.
B. Kitchenham. Guidelines for performing systematic literature reviews in software engeneering. Technical Report EBSE-2007-01, Keele Univ., UK, 2007.
C. Bouras and V. Tsogkas. Assisting cluster coherency via n-grams and clustering as a tool to deal with the new user problem. pages 1–14, 2014.
D. Bell, J. Guan, and Y. Bi. On combining classifier mass functions for text categorization. 17(10):1307–1319, Oct 2005.
D. Cai and X. He. Manifold adaptive experimental design for text categorization. 24(4):707–719, April 2012.
D. Godoy. Comparing one-class classification algorithms for finding interesting resources in social bookmarking systems. In Resource Discovery, volume 6799 of LNCS, pages 88–103. Springer, 2012.
D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems An Introduction. Cambridge Univ. Press, 2011.
F. Narducci, C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Exploiting big data for enhanced representations in content-based recommender systems. 152:182–193, 2013.
F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 1–35. Springer, 2011.
F. Sebastiani. Machine learning in automated text categorization. 34(1):1–47, 2002.
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. 17(6):734–749, 2005.
G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. 18(11):613–620, 1975.
H. L. Borges and A. C. Lorena. A survey on recommender systems for news data. In E. Szczerbicki and N. Nguyen, editors, Smart Inf. and Knowledge Management, volume 260 of Studies in Comput. Int., pages 129–151. Springer, 2010.
J. Yu, Y. Shen, and J. Xie. Mining user interest and its evolution for recommendation on the micro-blogging system. In J. Wang, H. Xiong, Y. Ishikawa, J. Xu, and J. Zhou, editors, Web-Age Information Management, volume 7923 of LNCS, pages 679–690. Springer, 2013.
K. F. Yeung and Y. Yang. A proactive personalized mobile news recommendation system. In J. of Internet Services Applications (2012), pages 207–212. IEEE, Sept. 2012.
L. Li, D. Wang, S. Zhu, and T. Li. Personalized news recommendation: A review and an experimental investigation. 26(5):754–766, 2011.
M. Diaby, E. Viennet, and T. Launay. Exploration of methodologies to improve job recommender systems on social networks. 4(1), 2014.
M. J. Pazzani and D. Billsus. The adaptive web. chapter Content-based Recommendation Systems, pages 325–341. Springer, Berlin, Heidelberg, 2007.
P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 73–105. Springer, 2011.
P. Resnick and H. R. Varian. Recommender systems. 40:56–58, March 1997.
Q. Zhu, Z. Li, H. Wang, Y. Yang, and M.-L. Shyu. Multimodal sparse linear integration for content-based item recommendation. In Multimedia (ISM), 2013 IEEE Int. Symp. on, pages 187–194, Dec 2013.
S. Cleger-Tamayo, J. M. Fernández-Luna, and J. F. Huete. Top-n news recommendations in digital newspapers. 27:180–189, 2012.
S. M. Peres, T. Rocha, M. R. C. B. Bíscaro, H. H., and C. Boscarioli. Tutorial sobre fuzzy-c-means e fuzzy learning vector quantizations: Abordagens híbridas para tarefas de agrupamento e classificação. 19(1):120–163, 2012.
S. Savage, M. Baranski, N. E. Chavez, and T. Hollerer. I’m feeling loco: A location based context aware recommendation system. In Advances in Location-Based Services: 8th International Symposium on Location-Based Services, Vienna 2011, LNGC. Springer, 2011.
S. Senecal and J. Nantel. The influence of online product recommendations on consumers’ online choices. 80(2):159–169, 2004.
S. Tantanasiriwong. A comparison of clustering algorithms in article recommendation system. In Proc. of SPIE - The Int. Soc. for Optical Eng., volume 8349, Singapore, 2012.
T. M. Mitchell. Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 1997.
W. Afzal, R. Torkar, and R. Feldt. A systematic review of search-based testing for non-functional system properties. 51(6):957–976, 2009.
W. Qu, K.-S. Song, Y.-F. Zhang, S. Feng, D.-L. Wang, and G. Yu. A novel approach based on multi-view content analysis and semi-supervised enrichment for movie recommendation. 28(5):776–787, 2013.
Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. 42(8):30–37, Aug. 2009.
Y. Wang, J. Liu, X. Dong, T. Liu, and Y. Huang. Personalized paper recommendation based on user historical behavior. In M. Zhou, G. Zhou, D. Zhao, Q. Liu, and L. Zou, editors, Natural Language Processing and Chinese Comp., volume 333 of Commun. in Comp. and Inf. Sci., pages 1–12. Springer, 2012.
Publicado
26/05/2015
Como Citar
BRUNIALTI, Lucas; PERES, Sarajane; FREIRE, Valdinei; LIMA, Clodoaldo.
Aprendizado de Máquina em Sistemas de Recomendação Baseados em Conteúdo Textual: Uma Revisão Sistemática. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 11. , 2015, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 203-210.
DOI: https://doi.org/10.5753/sbsi.2015.5818.
