Personalized clothing recommendation combining user social circle and fashion style consistency

With the rapid expansion of social networks and fashion websites, clothing recommendation has attracted more attention of researchers, since various web data bring opportunities for recommender systems to solve the problems of cold start and sparsity. For clothing recommender system, user social cir...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-07, Vol.77 (14), p.17731-17754
Main Authors: Sun, Guang-Lu, Cheng, Zhi-Qi, Wu, Xiao, Peng, Qiang
Format: Article
Language:English
Subjects:
Artificial neural networks
Cold starts
Computer Communication Networks
Computer Science
Consistency
Data Structures and Information Theory
Decision making
Designers
Handbags & purses
Multimedia Information Systems
Recommender systems
Social factors
Special Purpose and Application-Based Systems
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Summary:With the rapid expansion of social networks and fashion websites, clothing recommendation has attracted more attention of researchers, since various web data bring opportunities for recommender systems to solve the problems of cold start and sparsity. For clothing recommender system, user social circle and fashion style consistency of clothing items are two important factors, which have critical impacts on user decision making. In this paper, two practical problems are considered: how to visually analyze fashion style consistency between clothing items and how to implement personalized clothing recommendation by combining user social circle and fashion style consistency. To address the first problem, a Siamese Convolutional Neural Network (SCNN) with a novel sampling strategy is employed to measure the fashion style consistency of clothing items. It can learn a feature transformation from clothing images to a latent feature space, where the representations of clothing items with similar styles locate closer than those with different styles. For the second problem, three social factors (i.e., personal interest, interpersonal interest similarity and interpersonal influence) and fashion style consistency are fused into a unified personalized recommendation model based on probabilistic matrix factorization (PMF). To comprehensively evaluate our model, extensive experiments have been conducted on two real world datasets collected from a popular social fashion website, which demonstrate the effectiveness of the proposed method for personalized clothing recommendation.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-5245-1