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Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems.

, , и . Expert Syst. Appl., 39 (1): 1273-1283 (2012)

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Classification Based on Association Rules for Adaptive Web Systems., и . Innovations in Hybrid Intelligent Systems, том 44 из Advances in Soft Computing, Springer, (2008)Framework for the development of a personalised recommender system with integrated web-mining functionalities., , , и . Int. J. Comput. Appl. Technol., 33 (4): 312-319 (2008)Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems., , и . Expert Syst. Appl., 39 (1): 1273-1283 (2012)Mining quantitative class-association rules for software size estimation., , , и . ISCIS, стр. 199-204. IEEE, (2009)Web mining based framework for solving usual problems in recommender systems. A case study for movies' recommendation., , , , и . Neurocomputing, (2016)A System for Efficient Portfolio Management., , , , и . IDEAL, том 4881 из Lecture Notes in Computer Science, стр. 980-989. Springer, (2007)Information-Theoretic Measures for Meta-learning., , и . HAIS, том 5271 из Lecture Notes in Computer Science, стр. 458-465. Springer, (2008)Comparing the Use of Traditional and Associative Classifiers towards Personalized Recommendations., , и . SEKE, стр. 607-612. Knowledge Systems Institute Graduate School, (2008)Movie Recommendation Framework Using Associative Classification and a Domain Ontology., , , , и . HAIS, том 8073 из Lecture Notes in Computer Science, стр. 122-131. Springer, (2013)Mining semantic data for solving first-rater and cold-start problems in recommender systems., , , , и . IDEAS, стр. 256-257. ACM, (2011)