Abstract
The numerous web sites existing nowadays make available more information than a user can manage. Thus, an essential requirement of current web applications is to provide users with instruments for personalized selective retrieval of web information. In this paper, a procedure for making personalized recommendations is proposed. The method is based on building a predictive model from an association model of Web data. It uses a set of association rules generated by a data mining algorithm that discovers knowledge in an incremental way. These rules provide models with relevant patterns that minimize the recommendation errors.
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Moreno, M.N., García, F.J., Polo, M.J., López, V.F. (2004). Using Association Analysis of Web Data in Recommender Systems. In: Bauknecht, K., Bichler, M., Pröll, B. (eds) E-Commerce and Web Technologies. EC-Web 2004. Lecture Notes in Computer Science, vol 3182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30077-9_2
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DOI: https://doi.org/10.1007/978-3-540-30077-9_2
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