A Recommendation System Based on Balance Theory in Social Marketing

• Main Authors: Hui Ju Hsieh, 謝惠如
• Other Authors: Bang Ye Wu
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/10699214368104599267

碩士 === 國立中正大學 === 資訊工程研究所 === 102 === For the massive information world, recommender system is very important. It can help people to find useful information efficiently. Using different methods will cause different performances. Motivated by predictions and recommendations which are popular in social marketing, we introduce a new method to compute similarities to improve memory-base collaborative filtering (CF) and implement it on real world data. The goal is to recommend buyers some interesting stores which they will like. We transform the real word data with rating information into a bipartite signed graph and then compute the similarities by balance theory. We use evaluation metrics to verify our experimental results and show the effectiveness. The experimental results show that the recommendation is significant.