Context-Aware Recommender Systems: Challenges and Opportunities

ALI Waqar; SHAO Jie; KHAN Abdullah Aman; TUMRANI Saifullah

doi:10.3969/j.issn.1001-0548.2019.05.002

Volume 48 Issue 5

Oct. 2019

Article Contents

Article Navigation > Journal of University of Electronic Science and Technology of China > 2019 > 48(5): 655-673

ALI Waqar, SHAO Jie, KHAN Abdullah Aman, TUMRANI Saifullah. Context-Aware Recommender Systems: Challenges and Opportunities[J]. Journal of University of Electronic Science and Technology of China, 2019, 48(5): 655-673. doi: 10.3969/j.issn.1001-0548.2019.05.002

Citation:

ALI Waqar, SHAO Jie, KHAN Abdullah Aman, TUMRANI Saifullah. Context-Aware Recommender Systems: Challenges and Opportunities[J]. Journal of University of Electronic Science and Technology of China, 2019, 48(5): 655-673. doi: 10.3969/j.issn.1001-0548.2019.05.002

Context-Aware Recommender Systems: Challenges and Opportunities

doi: 10.3969/j.issn.1001-0548.2019.05.002

1.
School of Computer Science and Engineering,University of Electronic Science and Technology of China Chengdu 611731;
2.
Faculty of Information Technology,University of Lahore Lahore Pakistan 54000

Funds:

Supported by the National Natural Science Foundation of China under Grant(61672133)

More Information

Author Bio:
ALI Waqar (1980-),男,博士生,主要从事人工智能和数据挖掘方面的研究.
Received Date: 2019-07-01
Rev Recd Date: 2019-09-05
Publish Date: 2019-09-15

Abstract

In this review, we attempt to highlight major concepts, techniques, challenges and future trends of context-aware recommender systems in social and scientific domains. The primary objective of this paper is to sum up the most recent developments in this rich knowledge area. A set of techniques and major frameworks available for context-based recommender systems are classified and introduced. Along with classical content-based, collaborative filtering and matrix factorization based techniques, we investigate the most recent research areas, i.e., deep learning and fuzzy logic based methodologies. Finally, we close by portraying potential future research opportunities with respect to utilizing context information in recommendation process.
- recommender systems,
- context-aware,
- context elicitation,
- context integration

References

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Context-Aware Recommender Systems: Challenges and Opportunities

1. School of Computer Science and Engineering,University of Electronic Science and Technology of China Chengdu 611731;

2. Faculty of Information Technology,University of Lahore Lahore Pakistan 54000

Funds:

Supported by the National Natural Science Foundation of China under Grant(61672133)

Author Bio:

Received Date: 2019-07-01

Rev Recd Date: 2019-09-05

Publish Date: 2019-09-15

Key words:

Abstract: In this review, we attempt to highlight major concepts, techniques, challenges and future trends of context-aware recommender systems in social and scientific domains. The primary objective of this paper is to sum up the most recent developments in this rich knowledge area. A set of techniques and major frameworks available for context-based recommender systems are classified and introduced. Along with classical content-based, collaborative filtering and matrix factorization based techniques, we investigate the most recent research areas, i.e., deep learning and fuzzy logic based methodologies. Finally, we close by portraying potential future research opportunities with respect to utilizing context information in recommendation process.

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Reference (114)

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Context-Aware Recommender Systems: Challenges and Opportunities

doi: 10.3969/j.issn.1001-0548.2019.05.002

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