2023
DOI: 10.1108/el-11-2022-0252
|View full text |Cite
|
Sign up to set email alerts
|

A group recommender system for books based on fine-grained classification of comments

Abstract: Purpose The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web. Design/methodology/approach The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 45 publications
0
3
0
Order By: Relevance
“…The intricacy of deep learning models can enhance the efficacy of hierarchical clustering as a learning mechanism. The utilization of deep learning layers enables the detection of subtle details in the data that may otherwise go unnoticed in conventional clustering methods [4]. For instance, a combined deep learning approach with a hierarchical clustering model can identify how specific customers like certain items that share similar features but are not identical.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The intricacy of deep learning models can enhance the efficacy of hierarchical clustering as a learning mechanism. The utilization of deep learning layers enables the detection of subtle details in the data that may otherwise go unnoticed in conventional clustering methods [4]. For instance, a combined deep learning approach with a hierarchical clustering model can identify how specific customers like certain items that share similar features but are not identical.…”
Section: Introductionmentioning
confidence: 99%
“…Utilizing a distinctive and comprehensive understanding of rainfall prediction by analyzing climatic variables and applying hierarchical clustering evaluation can identify patterns within the data that can be utilized for future rainfall forecasting [16]. This approach is accomplished by training a deep-learning model on weather variables, such as temperature, air pressure, and wind speed, to identify correlations between weather patterns and rainfall occurrences [17]. The deep learning model can be taught by employing hierarchical clustering analysis to identify patterns to improve the precision of rainfall prediction.…”
Section: Introductionmentioning
confidence: 99%
“…The findings on the variations in the writing style of book reviews have implications for the development of linguistically-informed recommender systems that offer personalized reading suggestions: in an era where readers face a multitude of options, such systems have become increasingly important (Alharthi et al, 2018). Information about the diverse writing styles of user-generated book reviews could be leveraged, for example, to develop more effective book recommendation systems that target specific groups of readers with tailored communication strategies, as suggested by Ye et al (2023). Overall, the study not only expands our understanding of written communication on reading platforms but also has practical applications in improving book recommendation systems.…”
Section: Introductionmentioning
confidence: 99%