An Implementation and Comparison of Collaborative Filtering for Recommendations

• Main Authors: Hui-Ching Lin, 林慧菁
• Other Authors: Duen-Jen Liu
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/10153568295974138305

碩士 === 國立交通大學 === 資訊管理所 === 91 === The marketplace is dramatically changed nowadays so as customers’ various demands. Today, organizations must design business strategies in accordance with customers’ needs. Due to this circumstance, recommender system has formed and deployed. The feature and purpose of the recommender system is to filter probable preferred items among piles of information in a specific field and in turn to recommend to users. In that sense users are able to reduce time for searching information. However, in certain conditions the preciseness of results from the system is quite low that reveals the system’s incapability of making appropriate recommendations to users. This research implements a collaborative filtering recommender system, and attempts to provide recommendations of filtered product items according to users’ interest and preference. With effective recommendation services, the recommender system enables to improve its preciseness of the estimation and hence reaches the goal of personalized recommendation. Moreover, this research integrates content-based approach and category weighting adjustment approach with collaborative filtering recommender system. Experiment evaluations are conducted to compare various recommendation methods.