Name
Abstract | ||
|---|---|---|
Recently, it has become difficult for ordinary users to find their interests when facing massive information accompanied by the popularity and development of social networks. The recommendation system is considered to be the most promising way to solve the problem by developing a personalized interest model and pushing potentially interesting content to each user. However, traditional recommendation methods (including collaborative filtering, which is currently the most mature and widely used method) are facing challenges of data sparsity, diversity and more issues that are causing unsatisfactory performance. In this paper, we propose the WLRRS, a new recommendation system based on weighted linear regression models. Compared with traditional methods, the WLRRS has the best predictive accuracy (RMSE) and the best classification accuracy (F-measure) with less fluctuation. WLRRS also provides better time performance compared to the collaborative filtering method, which meets the requirements of the real production environment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compeleceng.2018.02.005 | Computers & Electrical Engineering |
Keywords | Field | DocType |
Recommendation systems,Linear regression,Weighted models,Accuracy | Recommender system,Collaborative filtering,Social network,Development environment,Computer science,Popularity,Mean squared error,Real-time computing,Artificial intelligence,Machine learning,Linear regression | Journal |
Volume | ISSN | Citations |
66 | 0045-7906 | 3 |
PageRank | References | Authors |
0.37 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenglong Li | 1 | 12 | 1.31 |
| zhaoguo wang | 2 | 132 | 8.56 |
| Shoufeng Cao | 3 | 3 | 0.37 |
| Longtao He | 4 | 19 | 2.51 |