Dynamic User Preferences Optimization in Time-aware Recommendations

Abstract

Recommender systems play a vital role in mitigating information overload by predicting user preferences. While traditional algorithms like collaborative filtering and content-based filtering have demonstrated their effectiveness, they often struggle to adapt to the dynamic nature of user preferences over time. This study addresses these limitations by enhancing the Time Correlation Coefficient (TCC) model with time-aware techniques, providing a more sophisticated understanding of the temporal shifts in user interests. We propose four advanced methodologies: Content-based Similarity, Time-based Decay, Cuckoo Search Optimization, and Decay Model Selection, each designed to improve recommendation accuracy by integrating dynamic, time-sensitive elements into the recommendation process. Our experiments reveal significant improvements in recommendation precision, demonstrating the advantages of these methodologies over the baseline TCC model in various performance metrics. The results emphasize the effectiveness of these dynamic strategies in personalizing user experiences, with a balanced approach to both accuracy and computational efficiency. This work lays a solid foundation for future research in recommendation technologies, offering practical insights and applications that can be extended across diverse domains. By enhancing recommender systems with a deeper understanding of temporal user behavior, we aim to improve the overall user experience in digital environments.