Objectives: To determine the significance of each object (node) in a graph, researchers often employ link analysis techniques, such as the Hyperlink-Induced Topic Search (HITS) algorithm. This will be performed for several reasons, including analyzing the confidentiality of social networks and optimizing search results based on the hierarchical nature of the Internet's interconnections. Methods: This work proposes a new version of HITS called the Boundary grading HITS method (BG-HITS). We offer a technique for calculating edge weights that uses just the graph's hub and authority parameters but considers the significance of each edge, its associated relationships and associations, and other relevant qualities such as whether or not they are "organization". Findings: Experiments on both simulated and realworld web-graph data demonstrate conclusively that our suggested method, when combined with edge weighting, may mitigate the effects of superfluous edges and nodes on the analysis, yielding more favourable and objective results than the previous HITS approach. Novelty: HITS is a method for doing link analysis that treats all edges the same in every calculation, much like nearly all other link analysis algorithms. The novelty of the proposed work is, the value of edges in practice varies from case to case and is influenced by the connections and associations between the two terminals. This has been resolved in the proposed approach.

The demand for smartphone apps has grown with the rising interest in artificial intelligence. Thanks to a vast number of applicant service applications, choosing the smartphone apps you want to use has been very complex for consumers. It is therefore essential that the customer interface is improved and that individual suggestions are made. Conventional recommendation approaches can in some cases be effective but have some drawbacks, which generally lead to unreliable recommendations. This study provides a basis for recommending smartphone applications, which is built on the algorithm of Hyperlink Induced Topic Search (HITS) in conjunction with association rule mining in this context. The approach combines the scores of authority and hub into the applications by means of downloads and ratings and not only takes into account the role of smartphone apps in alliance rules but also the trustworthiness aspect of consumers. Studies with industry data sets from the Samsung framework reveal that the proposed approach increases the recommendation precision greatly relative to conventional approaches.