Cognitive Computing Models For Personalized Recommendation Systems Using Behavioral And Contextual Data

Abstract

Recommendation systems that are personalized have also become an inseparable part of the contemporary online ecosystem, allowing smart content delivery and improved interaction with users in areas like e-commerce, streamlining services, healthcare, and online education. Conventional recommendation methods, at times, have the inherent limitations of lack of contextual knowledge, scant behavioural knowledge, and the lack of responsiveness to changing user preference. To overcome these limitations, this paper offers a personalized recommendation model of cognitive computing, which combines the behavioral and contextual data to enhance the accuracy of the recommendation and efficiency of decision-making. This framework leverages user behavioral data like browsing history, click patterns, frequency of interaction and purchase activities and contextual data like time, location, device type and user environment. An adaptive analysis of user preferences is done using a cognitive inference mechanism to create intelligent personalized recommendations. The methodology includes data preprocessing, feature extraction, integration of context, and machine learning-based recommendation generation to increase the performance of predictions and individualization of the process. The success of the suggested model is determined with popular recommendations accuracy measures such as Precision@K, Recall@K, F1-Score, and Normalized Discounted Cumulative Gain (NDCG). The experimental findings indicate that the proposed cognitive computing framework enhances the performance of the recommendation system in relevancy of the recommendations, ranking of quality and personalization in comparison with the traditional recommendation methods including collaborative filtering and content-based models. The results show that the combination of cognitive intelligence and behavioral and contextual analytics can significantly improve adaptive recommendation system. Future directions can include real-time optimization of recommendations, explainable artificial intelligence, models of personalization based on federated learning.