Name
Abstract | ||
|---|---|---|
In order to make recommendations to a user, a recommender mainly uses two approaches: content-basedfiltering approach and collaborative filtering approach. However, they both still have some shortcomings technically. The content-based approach is difficult to handle feature extraction as well as user intension prediction. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. In this paper, we present an novel hybrid recommendation approach based on Ontology and Neural Network in the movie domain. The approach combines content-based filtering and collaborativefiltering and a recommender can use them individually or use them both. The hybrid recommendation approach can tackle the traditional recommenders -- problems, such as feature extraction, intension prediction, matrix sparsity and cold start problems. Our experiments show that, our approach provides a good method to make recommendations to users. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GreenCom-CPSCom.2010.144 | GreenCom/CPSCom |
Keywords | DocType | Citations |
neural networks,cold start problem,collaborative approach,content-based approach,matrix sparsity problem,content-basedfiltering approach,matrix sparsity,intension prediction,feature extraction,novel hybrid recommendation approach,hybrid movie recommender,hybrid recommendation approach,ontologies,filtering,collaboration,ontology,collaborative filtering,neural nets,recommender systems,motion pictures,history,artificial neural networks,neural network | Conference | 4 |
PageRank | References | Authors |
0.43 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Deng | 1 | 4 | 1.44 |
| Zhonghai Wu | 2 | 48 | 11.55 |
| Cong Tang | 3 | 20 | 7.18 |
| Huayou Si | 4 | 9 | 1.54 |
| Hu Xiong | 5 | 144 | 16.81 |
| Zhong Chen | 6 | 503 | 58.35 |