Research Area:  Machine Learning
This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, which works on the idea of altering behavior of the user with respect to time. The proposed model considers solving two recommendation problems (fluctuating user interest over time and high computation time when dataset shifts from scarcity to abundance) and presents a real application of the proposed method in the field of recommendation engine. It describes a system which enrolls the changing behavior of user to furnish personalization suggestions. The results obtained by TimeFly are compared with the results of other well-known algorithms. Simulation results on 100K, 1M, 10M, and 20M MovieLens dataset reveal that using TimeFly leads to high accurate predictions in less computation time.
Author(s) Name:   Bam Bahadur Sinha, R. Dhanalakshmi & Ramchandra Regmi
Journal name:  Pattern Analysis and Applications
Publisher name:  Springer
Volume Information:  volume 23, pages 1727–1734 (2020)
Paper Link:   https://link.springer.com/article/10.1007/s10044-020-00883-8