Intelligent Recommendation Systems Using Personalized Preference Learning and Contextual Data

Noah Campbell

Intelligent Recommendation Systems Using Personalized Preference Learning and Contextual Data

Author(s)	Noah Campbell
Country	Canada
Abstract	Recommendation systems support users in navigating large collections of products, services, media, information, and digital resources by estimating which items are likely to be relevant to individual preferences. Conventional collaborative and content-based approaches primarily model relationships among users and items. However, user interests can change according to situational factors such as time, device, interaction history, session characteristics, and recent behavior. Intelligent recommendation therefore increasingly requires both personalized preference learning and contextual information. This hypothetical methodological study examines a recommendation framework that combines latent user-preference learning with contextual data. A fictional dataset containing 2,000 users, 5,000 items, and 60,000 interaction events is divided chronologically into hypothetical training, validation, and testing partitions. Three conceptual configurations are compared: a non-personalized popularity baseline, matrix-factorization-based personalized recommendation, and a contextual personalized recommendation model integrating temporal, device, and session characteristics. The illustrative analysis evaluates ranking quality using Precision@10, Recall@10, and NDCG@10, together with rating-prediction RMSE for a secondary preference-estimation task. Under the assigned hypothetical values, the personalized contextual configuration records the strongest overall ranking performance and the lowest prediction error. A second analysis evaluates the incremental inclusion of contextual signals and illustrates how time, device, and session information may alter recommendation quality. The study concludes that intelligent recommendation systems can benefit methodologically from combining long-term user preferences with situational information. Nevertheless, contextual data do not automatically improve recommendation and may introduce noise, sparsity, privacy concerns, and over-personalization. Reliable empirical evaluation should therefore use chronological testing, strong recommendation baselines, ranking-oriented metrics, and transparent analysis of individual contextual contributions.
Keywords	Recommendation Systems, Personalized Preference Learning, Context-Aware Recommendation, Collaborative Filtering, Matrix Factorization, User Modeling, Contextual Data, Ranking Prediction, Personalization, Intelligent Systems.
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2026
Published On	2026-02-15

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International Journal of Machine Learning, AI & Data Science Evolution

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Intelligent Recommendation Systems Using Personalized Preference Learning and Contextual Data

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