Original scientific paper
https://doi.org/10.1080/00051144.2022.2140389
A scheme of opinion search & relevant product recommendation in social networks using stacked DenseNet121 classifier approach
Murugesan Shanmugavelu
; Department of Information Technology Tagore Engineering College, Rathinamangalam, Chennai, India
*
Muthurajkumar Sannasy
; Department of Computer Technology, Anna University, Chennai, India
* Corresponding author.
Abstract
Traditional methods have resulted in lower-quality search results with a lower accuracy rate. This
problem is addressed and a recommended technique using deep learning methods is provided
with the goal of improving prediction quality. Via this paper, a novel paradigm for pertinent
product recommendations in social networks is provided. The major goal of this strategy is to
let computers learn automatically without any assistance from humans, consequently controlling operations as needed. The social input data set in this proposed study is first pre-processed
to remove noise. Following that, a Fisher discriminant method based on information is used for
feature extraction. Then, using the Hierarchical Agglomerative and Attribute-based Clustering
procedure, the features are chosen from the retrieved ones. Following that, such clusters are
predicted using the stacked DenseNet121 method, and Attention-based MLP is used to propose
the product. Finally, to verify the effectiveness of the suggested system, the expected output was
evaluated, the performance measure was examined, and comparisons with conventional methods were made. Out of 2033 reviews, the suggested approach has a positive score percentage of
92.22%. The investigation demonstrates that the suggested system is more effective at providing
improved results for pertinent product recommendations.
Keywords
Recommender system; relevant product recommendation; information based fisher discriminant algorithm; hierarchical agglomerative and attribute-based clustering; stacked DenseNet121; attention-based MLP
Hrčak ID:
315746
URI
Publication date:
8.11.2022.
Visits: 503 *