TimeFly algorithm: a novel behavior-inspired movie recommendation paradigm

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.

[1]  Xiaoyong Li,et al.  Clustering collaborative filtering recommendation system based on SVD algorithm , 2013, 2013 IEEE 4th International Conference on Software Engineering and Service Science.

[2]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[3]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[4]  R. Logesh,et al.  Exploring Hybrid Recommender Systems for Personalized Travel Applications , 2018, Cognitive Informatics and Soft Computing.

[5]  O. A. Mohamed Jafar,et al.  A Comparative Study of Hard and Fuzzy Data Clustering Algorithms with Cluster Validity Indices , 2013 .

[6]  Behzad Soleimani Neysiani,et al.  Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm , 2019, International Journal of Information Technology and Computer Science.

[7]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[8]  Yun Fu,et al.  Heterogeneous Recommendation via Deep Low-Rank Sparse Collective Factorization , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Fernando Ortega,et al.  A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model , 2016, Knowl. Based Syst..

[10]  Prateek Jain,et al.  Low-rank matrix completion using alternating minimization , 2012, STOC '13.

[11]  R. K. Pateriya,et al.  Collaborative Filtering Techniques in Recommendation Systems , 2019, Data, Engineering and Applications.

[12]  Arun K. Pujari,et al.  Proximal maximum margin matrix factorization for collaborative filtering , 2017, Pattern Recognit. Lett..

[13]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[14]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[15]  Juan-Zi Li,et al.  Typicality-Based Collaborative Filtering Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[16]  Inderjit S. Dhillon,et al.  Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems , 2012, 2012 IEEE 12th International Conference on Data Mining.

[17]  Ahmed Eldawy,et al.  LARS*: An Efficient and Scalable Location-Aware Recommender System , 2014, IEEE Transactions on Knowledge and Data Engineering.

[18]  G. James Blaine,et al.  Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer , 1975, Computer.

[19]  Sonal Jain,et al.  Journal Recommendation System Using Content-Based Filtering , 2019 .

[20]  Shie-Jue Lee,et al.  A clustering based approach to improving the efficiency of collaborative filtering recommendation , 2016, Electron. Commer. Res. Appl..

[21]  Tomaz Curk,et al.  Generating inter-dependent data streams for recommender systems , 2018, Simul. Model. Pract. Theory.

[22]  John Riedl,et al.  Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering , 2002 .

[23]  G. Karypis,et al.  Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems , 2002 .