A Comprehensive Recommender System Model: Improving Accuracy for Both Warm and Cold Start Users
Sparsity of the ratings available in the recommender system database makes the task of rating prediction a highly underdetermined problem. This poses a limit on the accuracy and the quality of prediction. In this paper, we utilize secondary information pertaining to user's demography and item c...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2015-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7361739/ |