Name
Abstract | ||
|---|---|---|
With the exponential growth of digital resource from Internet, search engines and recommendation systems have become the effective way to find relevant information in a short period of time. In recent years, advances in deep learning have received great attention in the fields of speech recognition, image processing, and natural language processing. The recommendation system is an important technology to alleviate information overload. How to integrate deep learning into the recommendation system, use the advantages of deep learning to learn the inherent essential characteristics of users and items from various complex multi-dimensional data, and build a model that more closely matches the user's interest needs has become a hotpot in the research field. This paper reviews the research and application status of recommendation algorithms based on deep learning, and tries to discusses and forecasts the research trends of deep learning approaches applied to recommendation systems. proceedings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33506-9_48 | Lecture Notes in Networks and Systems |
Field | DocType | Volume |
Recommender system,Information overload,Search engine,Computer science,Image processing,Artificial intelligence,Deep learning,Multimedia,Distributed computing,The Internet | Conference | 97 |
ISSN | Citations | PageRank |
2367-3370 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianhan Gao | 1 | 18 | 17.71 |
| Lei Jiang | 2 | 0 | 0.34 |
| Xibao Wang | 3 | 0 | 0.34 |