Abstract
In this paper, we focus on small or medium-sized e-commerce portals. Due to high competition, users of these portals are not too loyal and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. For this task, we propose a model coupling various implicit feedbacks and object attributes in matrix factorization. We report on promising results of our initial off-line experiments on travel agency dataset. Our experiments corroborate benefits of using object attributes; however we are yet to decide about usefulness of some implicit feedback data.
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Peska, L., Vojtas, P. (2014). Recommending for Disloyal Customers with Low Consumption Rate. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds) SOFSEM 2014: Theory and Practice of Computer Science. SOFSEM 2014. Lecture Notes in Computer Science, vol 8327. Springer, Cham. https://doi.org/10.1007/978-3-319-04298-5_40
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DOI: https://doi.org/10.1007/978-3-319-04298-5_40
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