A New Similarity Computation Method in Collaborative Filtering Based Recommendation System
A New Similarity Computation Method in Collaborative Filtering Based Recommendation System
- New search for: Wu, Xiaokun
- New search for: Huang, Yongfeng
- New search for: Wang, Shihui
- New search for: Wu, Xiaokun
- New search for: Huang, Yongfeng
- New search for: Wang, Shihui
Recently, collaborative filtering algorithm is widely used in recommendation systems. Similarity calculation is an important step of this algorithm. Some of the existing similarity computing methods can not accurately measure the similarity, and the accuracy needs to be improved. In this paper, a new similarity calculation method is proposed, which is called JacRA. In order to verify the effectiveness of our proposed method, we compare several methods based on MovieLens data sets. Experimental results show that our method outperforms the contrast methods in terms of accuracy.
Document information
A New Similarity Computation Method in Collaborative Filtering Based Recommendation System
2017-09-01
168054 byte
Conference paper
Electronic Resource
English
Similar titles
An Ontology-Based Collaborative Filtering Personalized Recommendation
| British Library Conference Proceedings | 2013
Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms
| British Library Conference Proceedings | 2005
A Collaborative Filtering Recommendation Algorithm Based on Product Clustering
| British Library Conference Proceedings | 2013
| British Library Online Contents | 2017