Time-Aware Recommender Systems: A Comprehensive Survey and Quantitative Assessment of Literature

Recommender systems (RS) are among the most widely used applications in data mining and machine-learning technologies. These technologies recommend relevant products to customers, such as movies to watch, items to buy, and books to read. The difference in user preferences over time is one of the most significant issues faced by recommender systems. Researchers have focused on time-sensitive recommender systems, and numerous studies have been conducted in this field. These studies aim to consider the time factor while offering recommendations to users by incorporating and utilizing temporal data in recommendations. In this work, we review existing works in this field and present the most prominent techniques and algorithms that have the ability to capture changes in user preferences over time, and the most important application areas for these recommendations. In addition, we present a quantitative assessment of comprehensive literature that investigates publications in terms of publication time, publication type, and datasets used. Finally, we highlight a range of findings and conclusions and provide the reader with insights based on a general analysis of time-sensitive recommender systems.