Garment recommendation in an e-shopping environment by using a Markov Chain and Complex Network integrated method
2021
Zhang, Junjie | Zeng, Xianyi | Dong, Min | Hong, Yan
In the e-commerce environment, website-based recommendation systems have been developed in order to help consumers without professional fashion-related knowledge to identify the most relevant garment products satisfying their specific personalized requirements in terms of fashion and functionalities. However, there are two main drawbacks in the existing recommendation systems: (1) the existing shopping data and/or human body data-based recommendations cannot effectively deal with fashion trends; (2) the shoppers’ experience and knowledge are rarely considered in these systems. To solve these two drawbacks, we propose in this paper a new garment recommendation mechanism using a Markov Chain and Complex Network integrated method. The Markov Chain method is employed as a suitable tool permitting one to extract fashion trends from a lower quantity of shopping data. The Complex Network method is used to formalize uncertain relations between human body shapes, garment fitting effects and garment features, provided by shopping experts. Compared with other existing recommendation methods, the proposed method is validated to be more robust and more interpretable owing to its capacity of handling body types and fashion trends.
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