Deep learning based recommender system : a survey and new perspectives
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/142804 |
| _version_ | 1811689512922775552 |
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| author | Zhang, Shuai Yao, Lina Sun, Aixin Tay, Yi |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Shuai Yao, Lina Sun, Aixin Tay, Yi |
| author_sort | Zhang, Shuai |
| collection | NTU |
| description | With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field. |
| first_indexed | 2024-10-01T05:49:17Z |
| format | Journal Article |
| id | ntu-10356/142804 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:49:17Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1428042020-07-01T07:03:33Z Deep learning based recommender system : a survey and new perspectives Zhang, Shuai Yao, Lina Sun, Aixin Tay, Yi School of Computer Science and Engineering Engineering::Computer science and engineering Information Systems Recommender Systems With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field. Accepted version 2020-07-01T07:03:33Z 2020-07-01T07:03:33Z 2019 Journal Article Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system : a survey and new perspectives. ACM Computing Surveys, 52(1), 5-. doi:10.1145/3285029 0360-0300 https://hdl.handle.net/10356/142804 10.1145/3285029 2-s2.0-85062450758 1 52 en ACM Computing Surveys © 2019 Association for Computing Machinery. All rights reserved. This paper was published in ACM Computing Surveys and is made available with permission of Association for Computing Machinery. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Information Systems Recommender Systems Zhang, Shuai Yao, Lina Sun, Aixin Tay, Yi Deep learning based recommender system : a survey and new perspectives |
| title | Deep learning based recommender system : a survey and new perspectives |
| title_full | Deep learning based recommender system : a survey and new perspectives |
| title_fullStr | Deep learning based recommender system : a survey and new perspectives |
| title_full_unstemmed | Deep learning based recommender system : a survey and new perspectives |
| title_short | Deep learning based recommender system : a survey and new perspectives |
| title_sort | deep learning based recommender system a survey and new perspectives |
| topic | Engineering::Computer science and engineering Information Systems Recommender Systems |
| url | https://hdl.handle.net/10356/142804 |
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