ARTICLE N PRESS J. Pinho Lucas et aL/ Expert Systems with Applications xxx(2011)xxx-xXX the CBa algorithm is more suitable to ployed for recom- Goksedef, M.& Gunduz-Oguducu, S.(2010). Combination of web page ender systems, because, as seen in Section 2, these systems are generally seriously affected by the sparsity drawback. Besides, ugh social agents In Fifth DELOS since new approaches for classification based on association are Han, J, Pei, J.& Yin, Y(2000). Mining frequent patterns without candidate generally related to memory usage and processing time, current generation In w. Chen, Naughton, &P A. Bernstein(Eds ) ACM SIGMOD intL associative classifiers would not supply many benefits to recom- Herlocker, J. L, Konstaseme data(pp. 1-12) ACM Press. 」(2004) mender systems(the recommender model is built off-line). In this ciently in recommender systems as a model based collaborative fil- Hill, w ems,22,5-53 context, we argue that CBa is very appropriate to be employed effi study conducted in this work present can be the basis of several fu- Huang. Z, Chen H. Zeng. D(2000. 4a P.94-201).New York, NY, USA: ACM tering method ture directions in recommender systems' research. First, because association rules may be easily combined with machine learning Koren, Y(2010). Collaborative filtering with temporal dynamics. Communications of methods. Hence, the CBA algorithm, for example, could be com- Lazcorreta E Botella, F,& Fernandez-Caballero, A(2008) Towards personalized bined to a content-based method in order to improve recommen- se, any other artificial intelligence technique could be used with CBA to bring up more personaliza- Lee, C-H, Kim. y-H.& Rhee, P-K(2001). Web personalization expert with tion. Alternatively, there are vast possibilities for extending an combining collaborative filtering and association rule mining technique Expert Systems and Appli associative classification approach for a specific domain imple- Li, W, Han, J-& Pei, J (2001) CMAR: Accurate and efficient classification based on mented in a recommender system. multiple class-association rules In ICDM (pp 369-376)- Liu, B, Hsu, W,&Ma. Y.(1998). Integrating classification and association rule Agrawal, R, Imielinski, T,& Swami, A(1 association rules between of data. Moreno. M nos, L, Garcia, FJ,& Toro, M.(2008). An association rule mining Ahn, H.J, Kang. H,& Lee, J(2010) Selecting a small number of products for Applications, 37(4). 3055-306 quality, development time and effort. Expert Systems with Applications, 34(1) Avery, C,& Zeckhauser, R (1997). Recommender systems for evaluating computer Neves, J.P. (2003). Ambiente de pos-processamento para regras de ass universidade do porto rogeneous models to predict consumer Rittman, M.(2005)."What is sparsity, and why should Balabanovic (1997). FAB: content-based, collaborative Sarwar, B. M, Karypis, G, Konstan, J. A,& Riedl,J.(2000). Analysis of recommendation. Communications of the ACM, 40(3). 66-72. tion algorithms for Billsus, D,& Pazzani, M. J.(1998) Learning collaborative. filters commerce(pp 158-167). Sarwar, B M, Karypis, G, Konstan, J. A, Reidl, (2001). Item-bas aborative ms for collaborative fil Schafer,」B.kon 1 conference on uncertainty in artificial intelligence(UAl-98)(pp. 43-52) Morgan Schafer, J. B (2005). The applic Data Mining and of data-mining to recommender systems. In]. Brin, S, Motwani, R, Ullman, J. D, Tsur, S(1997). Dynamic itemset counting and Wang(Ed Encyclopedia of data warehousing and mining Information Science ung. kawn. k wok. rn. ow. m. h. mter o -c. 2003 Mining customer product Sun, x, Kong, F,& Chen, H. (2005). Using quantitative association rules in n, Z. Wu, &). Yang(Eds ) WAIM. Lecture ratings for personalized omputer science(Vol. 3739, pp 822 Chou, P.H. Li, P.-H Chen, K-K& Wu, M-](2010) Integrating web mining and Thabtah, F, Cowling. P-& Peng. Y(2005) MCAR: Multi-class classification based or ed e-commerce automatic service Expert Systems in.M.(1999) J(2003).CPAR: Ciassification based er society, pp 33 association rules SIAM international conference on data mining(pp331-335). ondliff,MK, Lewis, D.D. Madigan, D, Posse C(1999) Bayesian mixed-effects Zaki, M J- Parthasarathy, $Ogihara, M,&Li, w(1997) New algorithms for fast ms and evaluation( Vol 99) Zhang F,& Chang, H-Y(2005). On a hybrid rule based re onnor, M,& Herlocker, ].(2001). Clustering items for collaborative filtering. In The fifth international conference on computer and information technology (pp. 94-198) Washington, DC, USA: IEEE Computer Socie Fawcett, T(2003) Roc graphs: Notes and practical considerations for data mining Ziegler, C-N McNee, S. M, Konstan, J. A,& Lausen, G(2005). Improving diversifcation. In 14rh international Gery, M.,& Haddad, H(2003). Evaluation of web usage mining approach World Wide Web conference(www 05 sers next request prediction. In The 5th ACM intermational workshop on Web information and data management, ACM, New York, NY, USA (pp. 74-81) Please cite this article in press as: Pinho Lucas, J, et al. Making use of associative classifiers in order to alleviate typical drawbacks in recommender sys tems.Expert Systems with Applications(2011). doi:10.1016/jeswa2011.07.136the CBA algorithm is more suitable to be employed for recom￾mender systems, because, as seen in Section 2, these systems are generally seriously affected by the sparsity drawback. Besides, since new approaches for classification based on association are generally related to memory usage and processing time, current associative classifiers would not supply many benefits to recom￾mender systems (the recommender model is built off-line). In this context, we argue that CBA is very appropriate to be employed effi- ciently in recommender systems as a model based collaborative fil￾tering method. The revision on recommender systems’ drawbacks and the case study conducted in this work present can be the basis of several fu￾ture directions in recommender systems’ research. First, because association rules may be easily combined with machine learning methods. Hence, the CBA algorithm, for example, could be com￾bined to a content-based method in order to improve recommen￾dations quality. Likewise, any other artificial intelligence technique could be used with CBA to bring up more personaliza￾tion. 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