Cloth Recommender System Based on Item Matching

Abstract

Abstract Recommender systems have become an essential part in our life. Most social Websites use recommender systems to enhance user experience. In online shopping Websites such as Amazon, Clothing is one of the most popular domains, therefore a recommender is of great significance. Previous recommender systems were often focused on retrieving items based on user preference, i.e. similarity to the previous items purchased by the user. However, in Clothing domain, the matching relationships between candidate items and the previously purchased is also important for recommendation. For example, a user may want to buy a new jean rather than suit pants if he/she has just purchased a shirt. This kind of matching relationships also frequently occurs in other life contexts. In this paper, we aim to recommend new clothes that can better match the clothes purchased by a user. This new recommendation strategy would work better in the Clothing domain and complement the current recommendation literature. Experiment results show that our method can lead to better recommendation performance in the Clothing domain.