Abstract
Recommender Systems are widely used in many areas such as entertainment, education, science, especially e-commerce. Integrating recommender system techniques to online shopping systems to recommend suitable products to users is really useful and necessary. In this work, we propose an approach for building an online shopping recommender system using implicit feedback from the users. For building the system, first we propose a method to collect the implicit feedback from the users. Then, we propose an ensemble method which combine several extended matrix factorization models which are specialized for those implicit feedback data. Next, we analyze, design, and implement an online system to integrate the aforementioned recommendation techniques. After having the system, we collect the feedback from the real users to validate the proposed approach. Results show that this approach is feasible and can be applied for the real systems.
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Anh-Thu, L.N., Nguyen, HH., Thai-Nghe, N. (2016). A Context-Aware Implicit Feedback Approach for Online Shopping Recommender Systems. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_57
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DOI: https://doi.org/10.1007/978-3-662-49390-8_57
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