Abstract
In this paper an approach to serendipitous item recommendation is outlined. The model used for this task is an extension of ProfileMatcher, which is based on fuzzy metadata describing both user and items to be recommended. To address the task of recommending serendipitous resources, a priori knowledge on the relations occurring among metadata values is injected in the recommendation process. This is achieved using fuzzy graphs to model similarity relations among the elements of the fuzzy sets describing the metadata. An experimentation has been carried out on the MovieLens data set to show the impact of serendipity injection in the item recommendation process.
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Dell’Agnello, D., Fanelli, A.M., Mencar, C., Minervini, M. (2011). Serendipitous Fuzzy Item Recommendation with ProfileMatcher. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2011. Lecture Notes in Computer Science(), vol 6857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23713-3_28
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DOI: https://doi.org/10.1007/978-3-642-23713-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23712-6
Online ISBN: 978-3-642-23713-3
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