Name
Abstract | ||
|---|---|---|
Many researchers have introduced tag information to recommender systems to improve the performance of traditional recommendation techniques. However, user-defined tags will usually suffer from many problems, such as sparsity, redundancy, and ambiguity. To address these problems, we propose a new recommendation algorithm based on deep neural networks. In the proposed algorithm, users' profiles are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space layer by layer. In this way, representations of the raw data will become more abstract and advanced, and therefore the unique structure of tag space will be revealed automatically. Based on those extracted abstract features, users' profiles are updated and used for making recommendations. The experimental results demonstrate the usefulness of the proposed algorithm and show its superior performance over the clustering based recommendation algorithms. In addition, the impact of network depth on the algorithm performance is also investigated. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.10.134 | Neurocomputing |
Keywords | Field | DocType |
Recommender systems,Tag information,Redundancy,Ambiguity,Deep neural networks | Recommender system,Data mining,Computer science,Raw data,Redundancy (engineering),Artificial intelligence,Cluster analysis,Artificial neural network,Ambiguity,Machine learning,Deep neural networks | Journal |
Volume | Issue | ISSN |
204 | C | 0925-2312 |
Citations | PageRank | References |
30 | 0.92 | 41 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zuo | 1 | 64 | 2.92 |
| Jiulin Zeng | 2 | 34 | 1.34 |
| Maoguo Gong | 3 | 2676 | 172.02 |
| Licheng Jiao | 4 | 5698 | 475.84 |