A FUZZY ASSOCIATIVE CLASSIFICATION APPROACH FOR RECOMMENDER SYSTEMS

作者：Pinho Lucas Joel; Laurent Anne; Moreno Maria N; Teisseire Maguelonne

来源：International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2012, 20(4): 579-617.

DOI：10.1142/S0218488512500274

• Recommender systems
• associative classification
• fuzzy association
• data mining

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative filtering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations%26apos; quality and effectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied effectively in recommender systems, minimizing the effects typical drawbacks they present.

• 出版日期2012-8