A Recommendation System Based on Balance Theory in Social Marketing
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 socia...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2014
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/10699214368104599267 |
| Summary: | 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.
|
|---|