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Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Korea
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Appl. Sci. 2019, 9(13), 2634; https://doi.org/10.3390/app9132634
Received: 1 May 2019 / Revised: 23 June 2019 / Accepted: 25 June 2019 / Published: 28 June 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method. View Full-Text
Keywords: apparel online shopping; personalized recommendation; implicit rating; collaborative filtering; random walk apparel online shopping; personalized recommendation; implicit rating; collaborative filtering; random walk
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Ok, M.; Lee, J.-S.; Kim, Y.B. Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping. Appl. Sci. 2019, 9, 2634.

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