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Future Internet 2018, 10(12), 117; https://doi.org/10.3390/fi10120117

A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
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Received: 28 October 2018 / Revised: 23 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
(This article belongs to the Special Issue Data Science for Internet of Things)
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Abstract

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms. View Full-Text
Keywords: collaborative filtering; comparison behavior; item-pairs; item-embedding; secondary-reordering collaborative filtering; comparison behavior; item-pairs; item-embedding; secondary-reordering
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Wang, B.; Ye, F.; Xu, J. A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce. Future Internet 2018, 10, 117.

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