원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.7 No.2
2014.04
pp.131-140
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
To improve the accuracy of memory based recommendation while keeping the low time cost, an expected item bias (EIA) based similarity computation is proposed. And a hybrid approach (HA) integrating the global rating information and local rating information is also proposed. The features of two classical datasets MovieLens and Netflix for recommendation system benchmarking are anglicized. The experiments on MovieLens and Netflix show that both EIA and HA could improve the performance alone. A combinational use of them will lead even better results on the two benchmark datasets.
목차
Abstract
1. Introduction
2. Related Work
3. Analysis on the Sparsity of Data
4. Similarity Computation
5. The Hybrid Approach for Recommendation
6. Experiments
6.1. Datasets
6.2. Evaluation
6.3. Results
7. Conclusions
References
1. Introduction
2. Related Work
3. Analysis on the Sparsity of Data
4. Similarity Computation
5. The Hybrid Approach for Recommendation
6. Experiments
6.1. Datasets
6.2. Evaluation
6.3. Results
7. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보