A scheme of opinion search & relevant product recommendation in social networks using stacked DenseNet121 classifier approach

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