Name
Abstract | ||
|---|---|---|
The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users' score data and interest's shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user's interest shift. User score is weighted according to the residual memory of forgetting function. Users' interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users' satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/info10040130 | INFORMATION |
Keywords | Field | DocType |
forgetting curve, combined recommendation, collaborative filter, similarity degree | Data mining,Forgetting,Residual,Collaborative filtering,Computer science,MovieLens,Mean absolute error,Algorithm,Forgetting curve,Sparse matrix | Journal |
Volume | Issue | Citations |
10 | 4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taoying Li | 1 | 18 | 4.68 |
| Linlin Jin | 2 | 0 | 0.34 |
| Zebin Wu | 3 | 8 | 7.58 |
| Yan Chen | 4 | 145 | 45.22 |