An Improved Personalized Collaborative Filtering Recommendation Based on Items Diversity

An Improved Personalized Collaborative Filtering Recommendation Based on Items Diversity
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Masoud Ghorbanian, Noor Hafizah Binti Hassan, Mohd Shahidan Bin Abdullah
DOI :  10.14445/22315381/IJETT-V69I12P203

How to Cite?

Masoud Ghorbanian, Noor Hafizah Binti Hassan, Mohd Shahidan Bin Abdullah, "An Improved Personalized Collaborative Filtering Recommendation Based on Items Diversity," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 13-20, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P203

Abstract
With the rapid information growth and the flood of data, users usually face a huge number of items. Therefore, finding desirable items is a challenge for users. On the other hand, users’ behavior is changing, and these changes are creating pressure on the profits of the popular items, while at the same time, the new or previously unknown items are being ignored due to lack of coverage in recommendations. In other words, the existing algorithms have not fully covered the diversity and coverage of the items in the recommendation lists. In this paper, a personalized recommendation algorithm is proposed to improve the diversity and coverage of the items. The contributions are as follows: a method is proposed to calculate the user’s diversity level in order to improve items` diversity and coverage in recommendations by performing a collaborative filtering method and k-means cluster based on genre. Also, intra-list diversity, coverage, and accuracy results are investigated between the proposed recommendation algorithm and other similar recommendation algorithms. The results of the experiment show that our proposed recommendation algorithm improves the diversity and coverage of the items significantly while still preserving the users’ interest.

Keywords
Accuracy, Collaborative filtering, Coverage, Diversity, the Recommendation algorithm

Reference
[1] F. Li, B. Wu, L. Xu, C. Shi, and J. Shi, A Fast-Distributed Stochastic Gradient Descent Algorithm for Matrix Factorization, JMLR: Workshop and Conference Proceedings 36(7787) (2014).
[2] Abdullah, Noraswaliza, Integrating collaborative filtering and matching-based search for product recommendation, Ph.D. thesis, Queensland University of Technology, Brisbane, Australia, (2012).
[3] MaizatulAkmar Ismail, Zahra Moghaddasi, Samira Sarvari, Ali Ebrahimi, Masoud Ghorbanian, Recommender Systems for Research Materials: An Overview, The 4th International Conference on Computer Science and Computational Mathematics, ICCSCM 2015, (2015) 349-354.
[4] SaikatBagchi, Performance and Quality Assessment of Similarity Measures in Collaborative Filtering Using Mahout, 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), 50 (2015) 229-234.
[5] Rena Nainggolan, ResiantaPerangin-angin, Emma Simarmata, and AstutiFerianiTarigan, Improved the Performance of the K-Means Cluster Using the Sum of Squared Error (SSE) optimized by using the Elbow Method8, 1st International Conference of SNIKOM, (2018) 23–24.
[6] F. Maxwell Harper and Joseph A. Konstan, The MovieLens Datasets: History and Context, ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (January 2016), (2016) 19.
[7] Marius Kaminskas and Derek Bridge, diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems, ACM Transactions on Interactive Intelligent Systems, 7(1) 2016.
[8] Rodrigues, C. M., Rathi, S., & Patil, G., An efficient system using item & user-based CF techniques to improve recommendation, 2016 2nd International Conference on Next Generation Computing Technologies, (2016).
[9] Eskandanian, F., Mobasher, B., & Burke, R., A clustering approach for personalizing diversity in collaborative recommender systems, Paper presented at the 25th Conference on User Modeling, Adaptation, and Personalization, Bratislava, Slovakia, (2017).
[10] Mehrjoo, S., Mehrjoo, M., &Hajipour, F., Optimal Diversity of Recommendation List for Recommender Systems based on the Users’ Desire Diversity, Journal of Information Science Theory and Practice, 7(3) (2019) 31–39.
[11] Ruipeng Li and Luiz Fernando Capretz, Assessing the Performance of Recommender Systems with MovieTweetings and MovieLens Datasets, International Journal of Innovation, Management, and Technology , 10(6) (2019) 229-234.
[12] Adzemovic H, Sandor A., Comparison of user and item-based collaborative filtering on sparse data, Stockholm, Sverige: Royal Institute of Technology, (2017).
[13] Castells, P., Hurley, N. J., & Vargas, S., Novelty and diversity in recommender systems, In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook,. New York: Springer, 881-918 (2015).
[14] S. Koras, M. Venkatarao Rao and V. Bhuvaneswari, Bi-directional Methodology for Literature Extraction from PubMed Abstracts using Web Scrapper and Web Crawler, International Conference on Communication and Electronics Systems (ICCES), (2019) 1978-1983.
[15] Eric K. Clemons Paul F. Nunes, Carrying your long-tail: Delighting your consumers and managing your operations, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, (2011).
[16] Tommaso Di Noia, Vito Claudio Ostuni, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio, An analysis of users` propensity toward diversity in recommendations, In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys `14). Association for Computing Machinery, New York, NY, USA, (2014) 285–288.
[17] Zuo, Y., Gong, M., Zeng, J., Ma, L., & Jiao, L., Personalized recommendation based on evolutionary multi-objective optimization, IEEE Computational Intelligence Magazine, 10 (2015) 52–62.
[18] Gogna, A., & Majumdar, A., Diablo: Optimization-based design for improving diversity in recommender system, Information Sciences, 378 (2017) 59–74.
[19] Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C., Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, 107 (2010) 4511– 4515.
[20] Kunaver, M., &Pozrl, T., Diversity in recommender systems–a survey, Knowledge-Based Systems, 123 (2017) 154–162.
[21] Shanfeng Wang, L. M. Q. C. L. J., Maoguo Gong, Decomposition based multiobjective evolutionary algorithm for collaborative filtering recommender systems, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), (2014) 672–679.
[22] Owen, S., Anil, R., Dunning, T., & Friedman, E., Mahout in Action, Greenwich, CT, USA: Manning Publications Co, (2011).
[23] Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems , 22( 1 ) (2004) 5–53.
[24] Sean M. McNee, John Riedl, and Joseph A. Konstan, Being accurate are not enough: how accuracy metrics have hurt recommender systems, CHI `06 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, (2006) 1097–1101.
[25] Asst.Prof.Johnsymol Joy, Overview of Different Data Clustering Algorithms for Static and Dynamic Data Sets, International Journal of Computer Science and Engineering (IJETT-IJCSE), (2018).