Comprehensive Review of Learnable and Adaptive Recommendation Systems

Dongre, Swati; Agrawal, Jitendra

doi:10.1007/978-981-19-4052-1_26

Cited by 1 publication

(1 citation statement)

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“…• E-learning E-learning advice systems created based on conventional e-learning systems have gained popularity among educational institutions. These programs help students choose classes, topics, and learning resources they are interested in and provide an engaging learning environment and online discussion forums [44].…”

Section: Healthcarementioning

confidence: 99%

Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions

Elzeheiry¹,

Gab-Allah²,

Mekky³

et al. 2023

Preprint

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Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

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Section: Healthcarementioning

confidence: 99%