Abstract:
In Fashion, Recommender System represents a growing trend. They enable to offer to the
customer online fully personalized shopping experience. Many known names on the Fashion
market such as Asos (asos.com) or Zalando (zalando.com), has already bet on this
technology to retain customers, leading to boosting profits. Multiple filtering approaches
exist, developed in order to cope with all the inherent challenges driven by the lack of
information and the ephemeral nature of fashion items. However, even if the methods
adopted are very varied, the main motive remains the same: reducing at all costs, the margin
of error in order to produce the nearest real prediction. Accuracy, scalability, flexibility, and
performance have become the keywords when it comes to creating a fully skilled
Recommender System. To achieve these objectives, it appears that including in the model
user‟s context information, such as time, location, mood, occasion, weather, or people‟s
influence can be the answer. Obviously, not including contextual information is eluding a
fundamental element in the decision-making process for the purchase of a particular piece of
clothing. In this paper, we decided to apply to the Fashion domain issues the scalable
context-aware algorithm to better target the tastes of the customer producing predictions as
close to his preferences as possible. This new algorithm named KFCR uses the kernel
mapping framework developed by Ghazanfar et al. (KMR) complements with contextual
information. We used the RentTheRunway Fashion dataset which indexes more than
100000 rented garments in the renttherunway.com website. We evaluated and compared this
new system to the original KMR , as well as to other widely used context-aware approaches
like post-filtering techniques. Once evaluated, the KFCR was found to be more accurate
than both non-context-aware and context-aware approaches.
