An interval-based Recommender System with Implicit Feedback

• Main Authors: Szu-Chi Chao, 趙思棋
• Other Authors: Ping-Yu Hsu
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/38301907600219816439

碩士 === 國立中央大學 === 企業管理研究所 === 99 === 　　The e-commerce is booming through rapid expansion of Internet. However, consumers can find many products and related information from the Internet, but also they face a problem to choose their right Internet within short time. Enterprises usually rely on a recommender system to strengthen the relationship with consumers, such as saving consumers’ search time, improving customer satisfaction and tracking the consumer behavior. Through recommender system enterprises can analyze the consumer behavior and product preferences and can provide the right goods to consumers to enhance the consumer''s purchase intention. However, most of the traditional recommender systems are static which identify consumers’ preferences by analyzing their historical data of the certain time period and those methods don’t consider a time factor that consumers’ preferences might change over time. This study propose a time-based content based approach for recommendation that considers time factors to the operation of the recommender system to improve the efficiency of the traditional recommender systems. The purpose of this study is to recommend the products that meet the consumers’ demand, by observing the changes of consumers’ transactions over time and identifying trends in consumers’ interests. We establish a model to convert the consumer transactions to the degree of preference and combine with the time factor. By using this model, we analyze trends in the consumer purchase interests and provide personal recommendation efficiently. This study uses the mobile service transaction database to assess the performance of the proposed approach. The experiment result shows that the proposed approach can predict the consumers’ purchase interest efficiently in the products’ category level.