Abstract
Collaborative filtering (CF) is one of the most popular techniques of the personalized recommendations, where CF generates personalized predictions in the rating matrix. The rating matrix typically contains a high percentage of unknown rating scores which is called the sparsity problem. The matrix factorization approach through temporal approaches has the accurate performance in addressing the sparsity issue but still with low accuracy. However, there are four issues when a factorization approach is adopted which are latent feedback learning, score overfitting, user’s interest drifting and item’s popularity decay over time. Therefore, this work introduces the temporal based factorization approach named TemporalMF++ to address all the issues. The experimental results show the TemporalMF++ approach has a higher prediction accuracy compared to the benchmark approaches. In summary, the TemporalMF++ approach has a superior effectiveness in improving the accuracy prediction of the CF by learning the temporal behaviour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hong, W., Li, L., Li, T.: Product recommendation with temporal dynamics. Expert Syst. Appl. 39(16), 12398–12406 (2012)
Bobadilla, J., Hernando, A., Ortega, F., Gutiérrez, A.: Collaborative filtering based on significances. Inf. Sci. (Ny) 185(1), 1–17 (2012)
Koenigstein, N., Dror, G., Koren, Y.: Yahoo! music recommendations : modeling music ratings with temporal dynamics and item taxonomy. In: Proceedings Fifth ACM Conference on Recommended Systems, pp. 165–172 (2011)
Mirbakhsh, N., Ling, C.X.: Clustering-based factorized collaborative filtering. In: Proceedings 7th ACM Conference Recommended Systems, pp. 315–318 (2013)
Al-hadi, I.A.A., Sharef, N.M., Sulaiman, N., Mustapha, N.: Ensemble Divide and Conquer Approach to Solve the Rating Scores’ Deviation in Recommendation System. J. Comput. Sci. Sci. Publ. (2016)
Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User Adap. Inter. 24(1–2), 67–119 (2014)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
Ye, F., Eskenazi, J.: Feature-based matrix factorization via long-and short-term interaction. Knowl. Eng. Manag. 473–484 (2014)
Yang, D., Chen, T., Zhang, W., Yu, Y.: Collaborative filtering with short term preferences mining. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR, vol. 2, p. 1043 (2012)
Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1–24 (2010)
Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl. Based Syst. 82, 163–177 (2015)
Bobadilla, J., Ortega, F., Hernando, A., Alcalá, J.: Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl. Based Syst. 24(8), 1310–1316 (2011)
Abdelwahab, A., Sekiya, H., Matsuba, I., Horiuchi, Y., Kuroiwa, S.: Feature optimization approach for improving the collaborative filtering performance using particle swarm optimization. Comput. Inf. Syst. J. 8(1), 435–450 (2012)
Shen, H., Zhu, Y., Zhou, X., Guo, H., Chang, C.: Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization. In: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 497–504 (2009)
Al-Hadi, I.A.A., Hashim, S.Z. M., Shamsuddin, S.M.H.: Bacterial foraging optimization algorithm for neural network learning enhancement. In: 2011 11th International Conference on, Hybrid Intelligent Systems (HIS), pp. 200–205 (2011)
Altingovde, I.S., Subakan, Ö.N., Ulusoy, Ö.: Cluster searching strategies for collaborative recommendation systems. Inf. Process. Manag. 49(3), 688–697 (2013)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Mirbakhsh, N., Ling, C.X.: Leveraging clustering to improve collaborative filtering. Inf. Syst. Front. 1–14 (2016)
Al-hadi, I.A.A., Sharef, N.M., Sulaiman, N., Mustapha, N.: Review of the temporal recommendation system with matrix factorization. Int. J. Innov. Comput. Inf. Control 13(5), 1579–1594 (2017)
Acknowledgements
The publication of this paper is sponsored by the Ministry of Higher Education, Malaysia under the Fundamental Research Grant Scheme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Al-Qasem, AH.I.A., Mohd Sharef, N., Nasir, S.M., Norwati, M. (2018). Temporal Based Factorization Approach for Solving Drift and Decay in Sparse Scoring Matrix. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-72550-5_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72549-9
Online ISBN: 978-3-319-72550-5
eBook Packages: EngineeringEngineering (R0)