FAiR: Framework de Avaliação e Caracterização em Sistemas de Recomendação
Resumo
Sistemas de Recomendação (SsR) tornaram-se essenciais em e-commerces. Estratégias de SsR vêm sendo propostas para auxiliar usuários na tomada de decisão. O processo de avaliação de SsR é hoje um grande ponto de divergência, uma vez que não existe um consenso de quais métricas são necessárias para se consolidar um novo SR. Nesse trabalho, realizamos um amplo estudo dessas métricas organizando-as em três grupos: Effectiveness-based, Complementary Dimensions of Quality e Domain Profiling. Consolidamos um framework denominado FAiR, capaz de auxiliar pesquisadores na avaliação de SsR frente a essas métricas, além de identificar as características das coleções de dados que possam intrinsecamente influenciar no desempenho deles.
Palavras-chave:
Caracterização, Métricas de Avaliação, Sistemas de Recomendação
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. IEEE transactions on knowledge and data engineering, 17(6):734–749.
Anderson, C. (2008). Long tail, the, revised and updated edition: Why the future of business is selling less of more.
Bobadilla, J., Ortega, F., Hernando, A., and Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46:109–132.
Cremonesi, P., Koren, Y., and Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of ACM RecSys, pages 39–46.
Ekstrand, M. D., Ludwig, M., Kolb, J., and Riedl, J. T. (2011). Lenskit: a modular recommender framework. In Proc. of ACM RecSys, pages 349–350.
Gantner, Z., Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2011). MyMedia-Lite: A free recommender system library. In Proc. of 5th ACM RecSys.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1):5–53.
Konstan, J. A. and Riedl, J. (1999). Research resources for recommender systems. In CHI99 Workshop Interacting with Recommender Systems.
Kouki, P., Fakhraei, S., Foulds, J., Eirinaki, M., and Getoor, L. (2015). Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems. In Proc. of ACM RecSys, pages 99–106.
Lee, K. and Lee, K. (2015). Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items. Expert Systems with Applications, 42(10):4851–4858.
Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74:12–32.
Modarresi, K. (2016). Recommendation system based on complete personalization. pages 2190 – 2204. ICCS 2016, California, USA.
Ozok, A. A., Fan, Q., and Norcio, A. F. (2010). Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. Behaviour & Information Technology, 29(1):57–83.
Puthiya Parambath, S. A., Usunier, N., and Grandvalet, Y. (2016). A coverage-based approach to recommendation diversity on similarity graph. In Proc. of ACM RecSys, pages 15–22.
Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction to recommender systems handbook. Springer.
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proc. ACM World Wide Web, pages 285–295.
Schwartz, B. (2009). The Paradox of Choice: Why More Is Less, Revised Edition. HarperCollins.
Vargas, S. and Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of ACM RecSys, pages 109–116.
Wu, W., He, L., and Yang, J. (2012). Evaluating recommender systems. In Proc. of IEEE ICDIM, pages 56–61.
Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., and Jambor, T. (2012). Auralist: introducing serendipity into music recommendation. In Proc. of ACM WSDM, pages 13–22.
Anderson, C. (2008). Long tail, the, revised and updated edition: Why the future of business is selling less of more.
Bobadilla, J., Ortega, F., Hernando, A., and Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46:109–132.
Cremonesi, P., Koren, Y., and Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of ACM RecSys, pages 39–46.
Ekstrand, M. D., Ludwig, M., Kolb, J., and Riedl, J. T. (2011). Lenskit: a modular recommender framework. In Proc. of ACM RecSys, pages 349–350.
Gantner, Z., Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2011). MyMedia-Lite: A free recommender system library. In Proc. of 5th ACM RecSys.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1):5–53.
Konstan, J. A. and Riedl, J. (1999). Research resources for recommender systems. In CHI99 Workshop Interacting with Recommender Systems.
Kouki, P., Fakhraei, S., Foulds, J., Eirinaki, M., and Getoor, L. (2015). Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems. In Proc. of ACM RecSys, pages 99–106.
Lee, K. and Lee, K. (2015). Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items. Expert Systems with Applications, 42(10):4851–4858.
Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74:12–32.
Modarresi, K. (2016). Recommendation system based on complete personalization. pages 2190 – 2204. ICCS 2016, California, USA.
Ozok, A. A., Fan, Q., and Norcio, A. F. (2010). Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. Behaviour & Information Technology, 29(1):57–83.
Puthiya Parambath, S. A., Usunier, N., and Grandvalet, Y. (2016). A coverage-based approach to recommendation diversity on similarity graph. In Proc. of ACM RecSys, pages 15–22.
Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction to recommender systems handbook. Springer.
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proc. ACM World Wide Web, pages 285–295.
Schwartz, B. (2009). The Paradox of Choice: Why More Is Less, Revised Edition. HarperCollins.
Vargas, S. and Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of ACM RecSys, pages 109–116.
Wu, W., He, L., and Yang, J. (2012). Evaluating recommender systems. In Proc. of IEEE ICDIM, pages 56–61.
Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., and Jambor, T. (2012). Auralist: introducing serendipity into music recommendation. In Proc. of ACM WSDM, pages 13–22.
Publicado
22/07/2018
Como Citar
CARVALHO, Diego; SILVA, Nícollas; ROCHA, Leonardo.
FAiR: Framework de Avaliação e Caracterização em Sistemas de Recomendação. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA DA SBC (CTIC-SBC), 37. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 41-50.
