The Personalized Recommender System for Kiosk in Fast Fashion Industry

• Main Authors: Chiu, Yen-Lin, 邱彥霖
• Other Authors: Chen, Mu-Chen
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/34596101502354736273

碩士 === 國立交通大學 === 交通運輸研究所 === 98 === Because of the rapid development of information technology, many clothing enterprises try to implement the concept of smart store. These enterprises introduce a number of technology applications to their physical stores. In addition, these enterprises take “Fast Fashion” as their business strategy so that their clothing products would be diversity and have a quite short life cycle. The Kiosk is a new facility in physical clothing store and there is no recommendation system for Kiosk. The traditional recommendation system is not an efficient way for Kiosk because of the Cold Start and over-specialization problem. The Cold Start problem will decrease the performance of recommendation system. The over-specialization problem can only focus on some products while making recommendation for customers. In order to solve the problems mentioned above, the recommendation system proposed in this study analyzes the nature of clothing products. The proposed system tries to learn about customers’ preferences for the nature of products and make recommendations. We can reduce the impact of Cold Start problem with this approach. This study solves the over-specialization problem by making recommendation lists based on association rule method. This proposed recommendation system that combine data mining, collaborative filtering and content-based filtering would apply to Kiosk in fast fashion industry. In this study, the architecture of the recommendation system is more flexible. According to the data and purposes, this recommendation system is divided into four sub-modules such as the branch transactions, customer data, transaction data and customer interaction data. Users can constitute the structure from distinct modules at will to meet the requirement in different situations. To provide personalized recommendations, system will analyze historical transactions and interaction data among users to learn their preferences and behaviors and then predicts what kind of the products users need. Not only provide a personalized and meaningfully ordered list, enterprises can also deduce and develop marketing and sales strategy from the recommendation system in this study. For example, For example, in case of the operating costs is not a limitation, enterprises can hold some bundle sales by taking customer preferences and association rules as reference. This strategy may strengthen customer’s purchase intention and improve business operation performance.