Kernel Fashion Context Recommender System (KFCR): A Kernel Mapping Fashion Recommender System Algorithm Using Contextual Information

In Fashion, Recommender System represents a growing trend. They enable to offer the customer online fully personalized shopping experience. Many known names on the Fashion market such as Asos (asos.com) or Zalando (zalando.com), have already bet on this technology to retain customers, boosting profits. Multiple filtering approaches exist, developed 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 the margin of error at all costs to produce the nearest reality prediction. Accuracy, scalability, flexibility, and performance have become the keywords when 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. 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 target the customer's tastes better, producing predictions as close to his preferences as possible. This new algorithm named KFCR uses the kernel mapping framework developed by (KMR) to complement context information. We evaluated and compared this new system to the Basic KMR, as well as to other widely used context-aware approaches like preand post-filtering techniques. Once evaluated, the KFCR was more accurate than both non-context-aware and context-aware approaches.