A Survey of Learning-Based Methods for Cold-Start, Social Recommendation, and Data Sparsity in E-commerce Recommendation Systems
16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021
; : 276-283, 2021.
Article
in English
| Scopus | ID: covidwho-1846123
ABSTRACT
With the continuous development of the economy and technology, people more and more rely on online shopping, especially during the pandemic of COVID19. On the other hand, sellers display many products, so customers need to make a great effort to find suitable products to meet their needs. To reduce the efforts of customers, researchers have developed many recommendation systems for online products. In this paper, to help further study recommendation systems in e-commerce, we survey the learning-based methods for solving the cold-start problem in a recommendation, social recommendation, and data sparsity. In particular, we compare these methods' pros and cons and point out the directions for further study. © 2021 IEEE.
cold-start; data sparsity; e-commerce; learning; recommendation system; social recommendation; Learning systems; Online systems; Recommender systems; Surveys; Continuous development; Customer need; E- commerces; E-commerce recommendations; Learning-based methods; Online shopping; Electronic commerce
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021
Year:
2021
Document Type:
Article
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