User Evaluation-Driven Ranking Concept

Abstract

Context. The problem of personalizing search engine results, empowering users with search result management tools and developing new ranking models based on user’s subjective information needs. The object of the study was to modeling information search results in the Internet based on user ratings.
 Objective. The goal of the work is to form unique expert groups for each user, based on calculating the measure of agreement between the current user’s opinions and potential experts.
 Method. Introducing a novel method for ranking search results based on user ratings, which takes a subjective approach to the ranking process. This approach involves the formation of distinct expert groups tailored to individual users. Experts are selected based on the level of agreement between their opinions and the current user, determined by shared ratings on a specific set of web resources. User selection for the expert group is based on their weight relative to the current user, serving as a measure of agreement.
 The proposed methodology offers a fresh approach to forming unique expert groups for each user, utilizing three different strategies depending on the presence of shared ratings on a particular set of web resources between the user and potential experts.
 The developed ranking method ensures that each user receives a personalized list of web resources with a distinct order. This is accomplished by incorporating unique ratings from the expert group members associated with each user. Furthermore, each rating contributes to the ranking model of web resources with an individual weight, calculated based on an analysis of their past system activity.
 Results. The developed methods have been implemented in software and investigated for complex web data operation in real time.
 Conclusions. The conducted experiments have confirmed the effectiveness of the proposed software and recommend its practical use for solving complex web data operation in real time. Prospects for further research may include optimizing software implementations and conducting experimental investigations of the proposed methods on more complex practical tasks of various nature and dimensions