Friend Recommendation Engine for Facebook Users via Collaborative Filtering
DOI:
https://doi.org/10.15837/ijccc.2023.2.4998Keywords:
Artificial Intelligence, Machine Learning, Recommender systems, Social MediaAbstract
Today’s internet consists of an abundant amount of information that makes it difficult for recommendation engines to produce satisfying outputs. This huge stream of unrelated data increases its sparsity, which makes the recommender system’s job more challenging. Facebook’s main recommendation task is to recommend a friendship connection based on the idea that a friend of a friend is also a friend; however, the majority of recommendations using this approach lead to little to no interaction. We propose a model using the matrix factorization technique that leverages interactions between Facebook users and generates a list of friendship connections that are very likely to be interactive. We tested our model using a real dataset with over 33 million interactions between users. The accuracy of the proposed algorithm is measured using the error rate of the predicted number of interactions between possible friends in comparison to the actual values.References
Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. Internet. Our World in Data, 2015. https://ourworldindata.org/internet.
Statista. Global digital population as of April 2022, 08 2022.
Michael D Ekstrand, John T Riedl, Joseph A Konstan, et al. Collaborative filtering recommender systems. Foundations and Trends® in Human-Computer Interaction, 4(2):81-173, 2011.
https://doi.org/10.1561/1100000009
J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. Collaborative filtering recommender systems. In The adaptive web, pages 291-324. Springer, 2007.
https://doi.org/10.1007/978-3-540-72079-9_9
Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. Content-based recommender systems: State of the art and trends. Recommender systems handbook, pages 73-105, 2011.
https://doi.org/10.1007/978-0-387-85820-3_3
Statista. Global social networks ranked by number of users 2022, 07 2022.
Aadil Alshammari and Abdelmounaam Rezgui. Cidf: A clustering-based interaction-driven friending algorithm for the next-generation social networks. IEEE Access, 7:153555-153565, 2019.
https://doi.org/10.1109/ACCESS.2019.2948948
Scott A Golder, Dennis M Wilkinson, and Bernardo A Huberman. Rhythms of social interaction: Messaging within a massive online network. In Communities and technologies 2007, pages 41-66. Springer, 2007.
https://doi.org/10.1007/978-1-84628-905-7_3
Christo Wilson, Bryce Boe, Alessandra Sala, Krishna Puttaswamy, and Ben Y Zhao. User interactions in social networks and their implications. In Proceedings of the 4th ACM European conference on Computer systems, pages 205-218, 2009.
https://doi.org/10.1145/1519065.1519089
Aadil Alshammari and Abdelmounaam Rezgui. Better edges not bigger graphs: An interactiondriven friendship recommender algorithm for social networks. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), pages 1-8. IEEE, 2020.
https://doi.org/10.1109/CloudTech49835.2020.9365918
Robin Burke. Hybrid web recommender systems. In The Adaptive Web, pages 377-408. Springer Berlin Heidelberg, 2007.
https://doi.org/10.1007/978-3-540-72079-9_12
Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, pages 73-105. Springer US, oct 2010.
https://doi.org/10.1007/978-0-387-85820-3_3
David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, dec 1992.
https://doi.org/10.1145/138859.138867
J. Ben Schafer, Joseph A. Konstan, and John Riedl. E-commerce recommendation applications. In Applications of Data Mining to Electronic Commerce, pages 115-153. Springer US, 2001.
https://doi.org/10.1007/978-1-4615-1627-9_6
Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, aug 2009.
https://doi.org/10.1109/MC.2009.263
Behnoush Abdollahi and Olfa Nasraoui. Explainable matrix factorization for collaborative filtering. In Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion. ACM Press, 2016.
https://doi.org/10.1145/2872518.2889405
Yue Shi, Martha Larson, and Alan Hanjalic. Mining contextual movie similarity with matrix factorization for context-aware recommendation. ACM Transactions on Intelligent Systems and Technology, 4(1):1-19, jan 2013.
https://doi.org/10.1145/2414425.2414441
Nidhi Kushwaha, Shubham Mehrotra, Ronish Kalia, Dhruv Kumar, and O. P. Vyas. Inclusion of semantic and time-variant information using matrix factorization approach for implicit rating of last.fm dataset. Arabian Journal for Science and Engineering, 41(12):5077-5092, may 2016.
https://doi.org/10.1007/s13369-016-2209-0
Guanzhong Liang, Chuan Sun, Jianing Zhou, Fengji Luo, Junhao Wen, and Xiuhua Li. A general matrix factorization framework for recommender systems in multi-access edge computing network. Mobile Networks and Applications, 27(4):1629-1641, 2022.
https://doi.org/10.1007/s11036-021-01869-4
Mehdi Hosseinzadeh Aghdam. A novel constrained non-negative matrix factorization method based on users and items pairwise relationship for recommender systems. Expert Systems with Applications, 195:116593, 2022.
https://doi.org/10.1016/j.eswa.2022.116593
Mario Casillo, Brij B Gupta, Marco Lombardi, Angelo Lorusso, Domenico Santaniello, and Carmine Valentino. Context aware recommender systems: A novel approach based on matrix factorization and contextual bias. Electronics, 11(7):1003, 2022.
https://doi.org/10.3390/electronics11071003
Ning Liu and Jianhua Zhao. Recommendation system based on deep sentiment analysis and matrix factorization. IEEE Access, 2023.
https://doi.org/10.1109/ACCESS.2023.3246060
Yong Wang, Mingxing Gao, Xun Ran, Jun Ma, and Leo Yu Zhang. An improved matrix factorization with local differential privacy based on piecewise mechanism for recommendation systems. Expert Systems with Applications, 216:119457, 2023.
https://doi.org/10.1016/j.eswa.2022.119457
Jaafar BenAbdallah, Juan C. Caicedo, Fabio A. Gonzalez, and Olfa Nasraoui. Multimodal image annotation using non-negative matrix factorization. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, aug 2010.
https://doi.org/10.1109/WI-IAT.2010.293
Behnoush Abdollahi and Olfa Nasraoui. A cross-modal warm-up solution for the cold-start problem in collaborative filtering recommender systems. In Proceedings of the 2014 ACM conference on Web science - WebSci '14. ACM Press, 2014.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Mohammed Alshammari, Aadil Alshammari
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
