Name
Title | ||
|---|---|---|
Improvement of Item-Based Collaborative Filtering by Adding Time Factor and Covering Degree |
Abstract | ||
|---|---|---|
Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system. It uses historical information to compute item-item similarity and make rating predictions. However, current IBCF approaches have a problem in that all items are accorded the same weight when computing the similarity and making predictions. To improve the quality of recommendations made by IBCF, we considered the memory habits of a customer and applied the covering-based rough set theory. In this research, we introduced a time-based correlation degree and applied it to the computation of item-item similarity, the covering degree was applied to make rating prediction. Our experimental results suggest that this novel approach produces recommendation results superior to those of existing work. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SCIS-ISIS.2016.0119 | 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) |
Keywords | Field | DocType |
Recommender system,Item-based collaborative filtering,time factor,covering-based rough sets | Recommender system,Data mining,Collaborative filtering,Computer science,Rough set,Time factor,Artificial intelligence,Machine learning,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5090-2679-1 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhipeng Zhang | 1 | 0 | 0.34 |
| Yasuo Kudo | 2 | 95 | 26.41 |
| Tetsuya Murai | 3 | 186 | 42.10 |