Abstract
This paper describes an approach to product recommendation that combines in a novel way content- and collaborative-based filtering techniques. The system helps the user to specify a query that filters out unwanted products in electronic catalogues (content-based). Moreover, if the query produces too many or no results, the system suggests useful query changes that save the gist of the original request. This process goes on iteratively till a reasonable number of products is selected. Then, the selected products are ranked exploiting a case base of recommendation sessions (collaborative-based). Among the user selected items the system ranks higher items that are similar to those selected by other users in similar sessions (twofold similarity). The approach has been applied to a web travel application and it has been evaluated with real users. The proposed approach: a) reduces dramatically the number of user queries, b) reduces the number of browsed products and c) the selected items are found first on the ranked list.
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Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M. (2003). Product Recommendation with Interactive Query Management and Twofold Similarity. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_37
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DOI: https://doi.org/10.1007/3-540-45006-8_37
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