2022
DOI: 10.22452/mjcs.vol35no2.2
|View full text |Cite
|
Sign up to set email alerts
|

An Aggrandized Framework for Enriching Book Recommendation System

Abstract: In this era of information overload, Recommender Systems have become increasingly important to assist internet users in finding the right choice from umpteen numbers of choices. Especially, in the case of book recommender systems, suggesting an appropriate book by considering user preferences can increase the number of book readers in turn having an aftereffect on the users’ lifestyle by reducing stress, stimulating imagination, improving vocabulary, and making readers smarter. The majority of book recommender… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 10 publications
(10 reference statements)
0
1
0
Order By: Relevance
“…This model made some grade-level predictions on the books too. In addition, Tulasi Prasad Sariki and G Bharadwaja Kumar [29] developed a new model to improve the recommendations generated in the book domain by using a judicious combination of the Natural Language Processing and Deep Learning techniques. The current study offered a three-module system to improve the suggestion process.…”
Section: Research Trends In Ebooks Content-based Filteringmentioning
confidence: 99%
“…This model made some grade-level predictions on the books too. In addition, Tulasi Prasad Sariki and G Bharadwaja Kumar [29] developed a new model to improve the recommendations generated in the book domain by using a judicious combination of the Natural Language Processing and Deep Learning techniques. The current study offered a three-module system to improve the suggestion process.…”
Section: Research Trends In Ebooks Content-based Filteringmentioning
confidence: 99%
“…Furthermore, it can be concluded that the suggested approach performs 6% better than the cutting-edge models, including the NCF with Content Embedding Model. An algorithm for digital library recommendations was presented by Fikadu Wayesa et al [18]. A hybrid book recommendation system using new user profile data was proposed in this study.…”
Section: Literature Reviewmentioning
confidence: 99%