Name
Abstract | ||
|---|---|---|
Recommendation systems are essential tools for piquing consumers’ interests and stimulating consumption in today’s electronic commerce, and the quality of these systems depends on the employed filtering algorithms. Therefore, improving the performance of these algorithms is an important issue. In this paper, we design an intensity-based contraction (IC) algorithm that works in combination with other machine-learning algorithms in model-based collaborative filtering, which is currently the most popular filtering algorithm. The
main challenges for this algorithm are sparseness of the database and lack of scalability. To demonstrate how IC is used, we implemented IC clustering as an example, which can effectively reduce the sparseness of the database and improve the efficiency. Moreover, we created a scalable
IC on a MapReduce model, the scalability of which is demonstrated with actual experiments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s12652-015-0284-9 | J. Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Recommendation system, Collaborative filtering, Electronic commerce, Intensity-based contraction, Scalability | Recommender system,Data mining,Collaborative filtering,Computer science,Filter (signal processing),Artificial intelligence,Cluster analysis,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
6 | 5 | 1868-5145 |
Citations | PageRank | References |
1 | 0.35 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baojiang Cui | 1 | 112 | 40.18 |
| Haifeng Jin | 2 | 36 | 5.87 |
| Zheli Liu | 3 | 356 | 28.79 |
| Jiangdong Deng | 4 | 9 | 2.53 |