Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System

Full Text (PDF, 405KB), PP.48-56

Views: 0 Downloads: 0


Abba Almu 1,* Aliyu Ahmad 1 Abubakar Roko 1 Mansur Aliyu 2

1. Department of Computer Science, Usmanu Danfodiyo University, Sokoto, Nigeria

2. Department of Computer Science, Sokoto State University, Sokoto, Nigeria

* Corresponding author.


Received: 26 Jan. 2022 / Revised: 10 Mar. 2022 / Accepted: 1 Apr. 2022 / Published: 8 Aug. 2022

Index Terms

Collaborative Filtering, Recommender System, Users' Input, Preference Changes, Recommended Items


The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to improve the quality of recommendations. The system solicits for users' inputs to create a user profiles. It then incorporates a set of new features (such as age and genre) to be able to predict user's preference changes with time. This enabled it to recommend movies to the users based on users new preferences. The experimental study conducted on Netflix and Movielens datasets demonstrated that, compared to the existing work, the proposed work improved the recommendation results to the users based on the values of Precision and RMSE obtained in this study which in turn returns good recommendations to the users.

Cite This Paper

Abba Almu, Aliyu Ahmad, Abubakar Roko, Mansur Aliyu, "Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System", International Journal of Information Technology and Computer Science(IJITCS), Vol.14, No.4, pp.48-56, 2022. DOI:10.5815/ijitcs.2022.04.05


[1]Rajani C., and Philippe L. A personalized Recommender System from Probabilistic Relational Model and Users’ Preferences. 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014, Procedia Computer Science, 4(11):469-473, 2014.
[2]Eyrun E., Gaurangi T., and Nan L. A Movie Recommendation System (MovieGEN). Unpublished., 2008.
[3]Manoj K., Yadav D., Ankur S., and Vijay K. A Movie Recommender System: MOVREC. International Journal of Computer Applications, 124(3):7-16, 2015.
[4]Rupali H., Ajinkya G., Kevin S., Jeet G., and Vrushal K. Moviemender- A Movie Recommender System, International Journal of Engineering Sciences & Research Technology, 5(11):469-473, 2016.
[5]Shreya Agrawal and Pooja Jain. An Improved Approach for Movie Recommendation System. International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2017) 3(17), 336-342, 2017.
[6]Muyeed A., Mir Tahsin I., and Raiyan K. Movie Recommendation System Using Clustering and Pattern Recognition Network. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC 2018). 143 –147, 2018.
[7]Meenu G., Aditya T., Vishal G., Dhruv P., and Singh R. Movie Recommender System Using Collaborative Filtering. Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), 415-420, 2020.
[8]Michael D. E., John T. R. and Joseph A. K.. Collaborative Filtering Recommender Systems. Foundation and Trends in Human-Computer interaction, 4(2), 81-173, 2010.
[9]Sanjeev D., Kulvinder S., and Naveen K.. Collaborative Filterng Based Product Recommendation System for Online Social Networks. International Journal of Computer Science, Engineering and Information Technology. 2(4):126-131, 2017.
[10]Harpreet K. V, Maninder S. and Amritpal S. Analysis and Design of Hybrid Online Movie Recommender System. International Journal of Innovations in Engineering and Technology (IJIET) 5(2), 159-162, 2015.
[11]Prerana K., and Shabnam P. Effective Hybrid Recommender Approach using Improved K-means And Similarity. International Journal of Computer Trends and Technology (IJCTT), 36(3):20-31, 2016.
[12]Ponnam L., Deepak P., Nallagulla S., & Yellamati S. Movie recommender system using Item-Based Collaborative Filtering Technique. 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) 1:55-60, 2016.
[13]Hafed Z., Ziad A., Mahmoud A., and Yaser J. A New Collaborative Filtering Recommendation Algorithm Based on Dimensionality Reduction and Clustering Techniques. 2018 9th International Conference on Information and Communication Systems (ICICS), 2(18):102-106, 2018.
[14]Badrul S., George K., Joseph K., and John R. Item-based Collaborative Filtering Recommendation Algorithms. WWW '01: Proceedings of the 10th international conference on World Wide Web. 285-295, 2001.
[15]Mohammed E,, Badr H and Abdelkader G. Building Recommendation Systems Using The Algorithms KNN and SVD. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 09(1):71-80, 2021.
[16]Almu A. and Bello Z.. An Experimental Study on the Accuracy and Efficiency of some Similarity Measures for Collaborative Filtering Recommender System. International Journal of Computer Engineering in Research Trends, 8(2), 33-3, 2021.
[17]GroupLens Research (2019). Movielens, retrieved from
[18]Roko, A., Almu, A., Mohammed, A., and Saidu, I. An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations. International Journal of Education and Management Engineering(IJEME), 10(1), 27-42, 2020.
[19]Harper, F., M., and Konstan, J., A. The MovieLens Datasets: History and Context.. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4), 1-19, 2015.
[20]Mingang C. and Pan L. Performance Evaluation of Recommender Systems. International Journal of Performability Engineering, 13(8):1246-1256, 2017.