Name
Title | ||
|---|---|---|
Deep hybrid recommender systems via exploiting document context and statistics of items. |
Abstract | ||
|---|---|---|
The sparsity of user-to-item rating data is one of the major obstacles to achieving high rating prediction accuracy of model-based collaborative filtering (CF) recommender systems. To overcome the obstacle, researchers proposed hybrid methods for recommender systems that exploit auxiliary information together with rating data. In particular, document modeling-based hybrid methods were recently proposed that additionally utilize description documents of items such as reviews, abstracts, or synopses in order to improve the rating prediction accuracy. However, they still have two following limitations on further improvements: (1) They ignore contextual information such as word order or surrounding words of a word because their document modeling methods use bag-of-words model. (2) They do not explicitly consider Gaussian noise differently in modeling latent factors of items based on description documents together with ratings although Gaussian noise depend on statistics of items. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ins.2017.06.026 | Information Sciences |
Keywords | Field | DocType |
Collaborative filtering,Document modeling,Deep learning,Contextual information,Gaussian noise,Item statistics | Data mining,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Recommender system,Obstacle,Word order,Collaborative filtering,Exploit,Statistics,Gaussian noise,Machine learning | Journal |
Volume | Issue | ISSN |
417 | C | 0020-0255 |
Citations | PageRank | References |
18 | 0.58 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Hyun Kim | 1 | 164 | 7.55 |
| Chanyoung Park | 2 | 163 | 12.04 |
| Jinoh Oh | 3 | 303 | 15.32 |
| Hwanjo Yu | 4 | 1715 | 114.02 |