Building Item–based Recommender System for Ladies' wear Personalization Service

Abstract

In recent years, the need for personalized service has been increased. However, personalization services must be improved to lighten user's burden in the process of personalization and produce results that are more adaptable. As one of the most promising approaches to improve the current personalized services, recommender systems have emerged in domains such as E-commerce, digital libraries. In our work, we firstly attempted to apply a recommender system in the field of E-commerce applications. Then, we decided to build a recommender system for Ladies' wear personalization services to make it more user-friendly and user-adaptive. One of the most successful technologies for recommender system is collaborative filtering. The bottleneck in conventional collaborative filtering algorithm (such as traditional user-based algorithm) is the search for neighbors among a large user population of potential neighbors. Our system uses item-based algorithm to avoid this bottleneck. The algorithm explores the relationships between items first rather than the relationship between users. Because the relationships between items are relatively static, item-based algorithms may be able to provide the same quality as the user-based algorithms with less online computation.