Adaptive Hyperlink‐Induced Topic Search‐Based Deep Learning Model for Topic Refinement and Modeling Using BERT With Residual Generative Network

Due to the rapid growth of online and technology development, every people have an own mobile to search their doubts and easily find it out within a second. Hypertext induced topic search (HITS) is a link analyzed algorithm utilized to find reliable websites that satisfy broad‐topic queries. To identify hub page that link to different authority pages and enhance better topic search recommendations, various conventional approaches have been proposed in recent times. Yet, these methods consume more computational duration and complexity ranges in the training and testing performance, enhancing poor performance. Moreover, it fails to manage with high‐dimensional large quantity of input noisy data, resulting inaccurate outcomes. Therefore, a novel deep learning‐based HITS model is developed to capture complex patterns in sparse, sequential and contextual data, thereby enabling personalized, dynamic topic search recommendations that customize the individual preferences in social media platforms. The applications of the adaptive HITS (AHITS) algorithm precisely focus on effective hyperlink selection. While analyzing the specific topics, the relevance of content in HITS algorithm is specifically improved using the random search‐based sharpbelly fish optimization (RS‐SBFO) strategy is designed. Then, the link contents are processed in the preprocessing phase. Here, the provided contents are preprocessed to eliminate noise and other related artifacts. Then, the acquired preprocessed contents are provided to the developed multihead attention‐based bidirectional encoder representations from transformer with residual generative long short‐term memory (MHA‐BRGLSTM) model for topic modeling and refinement. Here, the developed MHA‐BRGLSTM provides an effective decision regarding the specific topics and related analysis by utilizing deep context analysis. Thus, the acquired outcomes are topic modeled and refined. Experimental validations are conducted to compare the effectiveness of the developed framework.