Modeling Field-Level Factor Interactions for Fashion Recommendation

Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due to the sparsity of the interaction data and the diversity of user preference in fashion. To tackle the challenge, this...

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Bibliographic Details
Main Authors: Ding, Yujuan, Mok, P.Y., Yang, Xun, Zhou, Yanghong
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Attribute Incorporation
Benchmark testing
Data models
Electronic commerce
Factor Interaction Modeling
Image color analysis
Personalized Fashion Recommendation
Predictive models
Online Access:Request full text
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Summary:Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due to the sparsity of the interaction data and the diversity of user preference in fashion. To tackle the challenge, this paper investigates multiple factor fields in fashion domain, such as colour, style, brand, and tries to specify the implicit user-item interaction into field level. Specifically, an attentional factor field interaction graph (AFFIG) approach is proposed which models both the user-factor interactions and cross-field factors interactions for predicting the recommendation probability at specific field. In addition, an attention mechanism is equipped to aggregate the cross-field factor interactions for each field. Extensive experiments have been conducted on three E-Commerce fashion datasets and the results demonstrate the effectiveness of the proposed method for fashion recommendation. The influence of various factor fields on recommendation in fashion domain is also discussed through experiments.
ISSN:1945-788X
DOI:10.1109/ICME52920.2022.9859989