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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66160

GROUP-ORIENTED LOCATION RECOMMENDATION SYSTEM (GOLRS) USING MULTI-AGENT INDUCED COGNITIVE BEHAVIORAL MODEL

V.VENKATA PRAVEEN KUMAR, Dr. S.PAZHANIRAJAN, Dr. INDRANEEL SREERAM

Abstract

Nowadays, there is a tremendous increase in location recommendation due to the development and use of location prediction process based on social networks. In this approach, a new multi-agent based framework is developed to generate personalized excellent location recommendations. The personalization problem is solved based on a dynamic user profile that includes the permanent and temporary cognitive behavior of the user. A better adaptation to the user's cognitive behavior improves the prediction processes and also improves the overall user experience with better results. Public acceptance of location-based social networking services has been greatly enhanced by the development of smart mobile devices. Location-Based Social Networks (LBSNs) are used as an important approach to exploit users locations based on their needs. Since human beings are very methodical in their behavior, activities are very important in one's life. The variety of human habits and preferences makes it difficult to find comfortable spaces for all people, whether in a group or not. Context-aware group-based location recommendation systems are developed from a random walk algorithm through this approach. The three different contexts considered from the developed approach are: location context (i.e., category, popularity, ability and spatial proximity), environmental context (i.e., weather, day of the week, social relationships, personal preferences). From the results it can be observed that the accuracy, F1 score increases and the total system takes less time to operate.

Keywords

Recommender neural network, Point Of Interest (POI), Location Based Social Networks (LBSNs), Information Retrieval (IR), World Wide Web (WWW).

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