A Survey on Recommendation System Techniques
DOI:
https://doi.org/10.56919/usci.2322.012Keywords:
Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Knowledge-based recommendation systems, e-commerce, Hybrid recommendation systemAbstract
The primary objective of recommender systems (RS) is to analyze user behavior and propose relevant items or services that users would find appealing. Recommender systems have gained significant prominence in various domains such as information technology and e-commerce. They achieve this by customizing recommendations based on individual preferences, efficiently filtering options from a vast pool, and enabling users to discover content that matches their interests. Numerous recommendation techniques have been developed to generate personalized suggestions, including collaborative filtering, content-based filtering, knowledge-based recommendation systems, and other approaches. Furthermore, hybrid recommendation systems have been proposed to address the limitations of individual methods by combining different techniques. This paper presents an overview of diverse recommendation methods, their fundamental approaches, challenges, solution and have equally looked at different solutions to these challenges faced by modern recommender systems. It also recommends promising avenues for future directions.
References
Aggarwal, C. C. (2016). Knowledge-based recommender systems. In Recommender systems (pp. 167-197). Springer. https://doi.org/10.1007/978-3-319-29659-3
Al Hassanieh, L., Abou Jaoudeh, C., Abdo, J. B., & Demerjian, J. (2018). Similarity measures for collaborative filtering recommender systems. In 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM) (pp. 1-5). IEEE. https://doi.org/10.1109/MENACOMM.2018.8371003
Alamdari, P. M., Navimipour, N. J., Hosseinzadeh, M., Safaei, A. A., & Darwesh, A. (2020). A systematic study on the recommender systems in the e-commerce. IEEE Access, 8, 115694-115716. https://doi.org/10.1109/ACCESS.2020.3002803
Alhijawi, B., & Kilani, Y. (2016). Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICIS.2016.7550751
Benouaret, I., & Amer-Yahia, S. (2020). A comparative evaluation of top-n recommendation algorithms: Case study with total customers. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4499-4508). IEEE. https://doi.org/10.1109/BigData50022.2020.9378404
Bobadilla, J., Alonso, S., & Hernando, A. (2020). Deep learning architecture for collaborative filtering recommender systems. Applied Sciences, 10(7), 2441. https://doi.org/10.3390/app10072441
Bobadilla, J., Ortega, F., Gutiérrez, A., & Alonso, S. (2020). Classification-based deep neural network architecture for collaborative filtering recommender systems. International Journal of Interactive Multimedia & Artificial Intelligence, 6(1). https://doi.org/10.9781/ijimai.2020.02.006
Bradley, K., & Smyth, B. (2003). Personalized information ordering: A case study in online recruitment. Knowledge-Based Systems, 16(5), 269-275. https://doi.org/10.1016/S0950-7051(03)00028-5
Cabezas, R., Ruiz, J. G., & Leyva, M. (2017). A knowledge-based recommendation framework using SVN. Neutrosophic Sets and Systems, 16, 24.
Cheng, C., Zhang, F., Lee, V. E., Jin, R., Garg, S., Choo, K. K. R., ... & Dong, L. (2019). Privacy-aware smart city: A case study in collaborative filtering recommender systems. Journal of Parallel and Distributed Computing, 127, 145-159. https://doi.org/10.1016/j.jpdc.2017.12.015.
Cheolsoo Park, C. C. T. J.-K. S. (2018). Review of the book Ontology-Based Recommender Systems. Business and Economics Journal, 9(2), 2151-6219.
Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies (IJCSIT), 7(3), 1174-1179.
Dong, M., Zeng, X., Koehl, L., & Zhang, J. (2020). An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences, 540, 469-488. https://doi.org/10.1016/j.ins.2020.05.094
Elahi, M., Ricci, F., & Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20, 29-50. https://doi.org/10.1016/j.cosrev.2016.05.002
Fumo, D., 2017. https://towardsdatascience.com. [Online] Available at: https://towardsdatascience.com/a-gentle-introduction-to-neural-networks-series-part-12b90b87795bc [Accessed 18 July 2018]
Gazdar, A., & Hidri, L. (2020). A new similarity measure for collaborative filtering based recommender systems. Knowledge-Based Systems, 188, 105058. https://doi.org/10.1016/j.knosys.2019.105058
Ge, M., Delgado- attended, C,. & Jonnach, D. (2010). Beyond accuracy: evaluating recommender systems by coverage and serendipity. proceedings of the fourth ACM Conference on Recommender Systems, 257-260. https://doi.org/10.1145/1864708.1864761
George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642. https://doi.org/10.1016/j.compedu.2019.103642
Hassan, M., & Hamada, M. (2016). Enhancing learning objects recommendation using multi-criteria recommender systems. IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). 62-64. https://doi.org/10.1109/TALE.2016.7851771
He, M., Delgado- attended, C,. & Jonnach, D. (2010). Beyond accuracy: evaluating recommender systems by coverage and serendipity. proceedings of the fourth ACM Conference on Recommender Systems, 257-260.
He, X., & Ke, X. (2021). Research summary of recommendation system based on knowledge graph. In The 2021 3rd International Conference on Big Data Engineering (pp. 104-109).mhttps://doi.org/10.1145/3468920.3468935
Hrnjica, B., Music, D., & Softic, S. (2020). Model-based recommender systems. In Trends in Cloud-based IoT (pp. 125-146). https://doi.org/10.1007/978-3-030-40037-8_8
Huang, Z., Xu, X., Ni, J., Zhu, H., & Wang, C. (2019). Multimodal representation learning for recommendation in Internet of Things. IEEE Internet of Things Journal, 6(6), 10675-10685. https://doi.org/10.1109/JIOT.2019.2940709
Ibrahim, A. J., Zira, P., & Abdulganiyyi, N. (2021). Hybrid recommender for research papers and articles. International Journal of Intelligent Information Systems, 10(2), 9. https://doi.org/10.11648/j.ijiis.20211002.11
Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, & Guangquan Zhang (2019). Recommender System Application Developments: A Survey. Physics Reports, 519, 1-49
Kaminskas, M., & Bridge, D. (2016) 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), 2. https://doi.org/10.1145/2926720
Li, B., Maalla, A., & Liang, M. (2021). Research on recommendation algorithm based on e-commerce user behavior sequence. In 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 2, pp. 914-918). IEEE. https://doi.org/10.1109/ICIBA52610.2021.9688086
Li, X., & Sun, F. (2021). Sports training recommendation method under the background of data analysis. In 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) (pp. 12-16). IEEE. https://doi.org/10.1109/HPBDIS53214.2021.9658481
Lipi Shah, Hetal Gaudani, & Prem Balani. (2016). Survey on Recommendation System. International Journal of Computer Applications, 137(7). https://doi.org/10.5120/ijca2016908821
Lops, P., Gemmis, M. d., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105). https://doi.org/10.1007/978-0-387-85820-3_3
Lu, J., Wang, M., Jiang, B., & Li, J. (2018). A personalized recommendation system with combinational algorithm for online learning. Journal of Ambient Intelligence and Humanized Computing, 9(3), 667-677. https://doi.org/10.1007/s12652-017-0466-8
Mittal, D., Shandilya, S., Khirwar, D., & Bhise, A. (2020). Smart billing using content-based recommender systems based on fingerprint. In ICT Analysis and Applications (pp. 85-93). Springer. https://doi.org/10.1007/978-981-15-0630-7_9
Mohammed Zabeeulla A., & Chandrasekar Shastry (2019). Career Recommendation System Design by Adopting Machine Learning Techniques. International Journal of Research in Electronics and Computer Engineering, 7(1).
Musto, C., Semeraro, G., Gemmis, M. d., & Lops, P. (2016). Learning word embeddings from Wikipedia for content-based recommender systems. In European conference on information retrieval (pp. 729-734). Springer. https://doi.org/10.1007/978-3-319-30671-1_60
Nilashi, M., Ibrahim, O. b., & Ithnin, N. (2014). Multi-criteria collaborative filtering with high accuracy using higher-order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 60, 82-101. https://doi.org/10.1016/j.knosys.2014.01.006
Numnonda, T. (2018). A real-time recommendation engine using lambda architecture. Artificial Life and Robotics, 23(2), 249-254. https://doi.org/10.1007/s10015-017-0424-8
Nunes, I., & Jannach, D. (2020). Knowledgeable explanations for recommender systems. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 657-660).
Ray, S. (2017, September 29). Commonly used Machine Learning Algorithms. Retrieved from Analytic Vidhya: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#targetText=Broadly%2C%20there%20are%203%20types%20of%20Machine%20Learning%20Algorithms&targetText=Examples%20of%20Supervised%20Learning%3A%20Regression,%2C%20KNN%2C%20Logistic
Resnick, P., Iacovou, N., Suchak, M., & Bergstrom, P. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (pp. 175-186). ACM. https://doi.org/10.1145/192844.192905
Rezaimehr, F., & Dadkhah, C. (2021). A survey of attack detection approaches in collaborative filtering recommender systems. Artificial Intelligence Review, 54(3), 2011-2066. https://doi.org/10.1007/s10462-020-09898-3
Ribeiro, M. T., Lacerda, A., Veloso, A., & Ziviani, N. (2012). Pareto-efficient hybridization for multi-objective recommender systems. In Proceedings of the sixth ACM conference on Recommender systems (pp. 19-26). https://doi.org/10.1145/2365952.2365962
Ricci, F., Rokach, L., & Shapira, B. (Eds.). (2015). Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-1-4899-7637-6
Shambour, Q., Lu, J., & Niu, Z. (2011). A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services. International Journal of Intelligent Systems, 26, 814-843. https://doi.org/10.1002/int.20495.
Sharma, V., Kumar, A., Lakshmi Panat, D., &Karajkhede, G., (2015). Malaria outbreak Prediction Model using Machine Learning. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4.
Shishehchi, S., Banihashem, S. Y., Zin, N. A. M., Noah, S. A. M., & Malaysia, K. (2012). Ontological approach in knowledge-based recommender system to develop the quality of e-learning system. Australian Journal of Basic and Applied Sciences, 6(2), 115-123.
Shokeen, J., & Rana, C. (2020). A study on features of social recommender systems. Artificial Intelligence Review, 53(2), 965-988. https://doi.org/10.1007/s10462-019-09684-w
Smiti, A. (2020). When machine learning meets medical world: Current status and future challenges. Computer Science Review, 37, 100280. https://doi.org/10.1016/j.cosrev.2020.100280
Tarus, J. K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21-48. https://doi.org/10.1007/s10462-017-9539-5
Volkovs, M., Yu, G. W., & Poutanen, T. (2017). Content-based neighbor models for cold start in recommender systems. In Proceedings of the Recommender Systems Challenge 2017 (pp. 1-6). https://doi.org/10.1145/3124791.3124792
West, J. D., Wesley-Smith, I., & Bergstrom, C. T. (2016). A recommendation system based on hierarchical clustering of an article-level citation network. IEEE Transactions on Big Data, 2(2), 113-123. https://doi.org/10.1109/TBDATA.2016.2541167
Xiao, J., Wang, M., Jiang, B., & Li, J. (2018). A personalized recommendation system with combinational algorithm for online learning. Journal of Ambient Intelligence and Humanized Computing, 9(3), 667-677. https://doi.org/10.1007/s12652-017-0466-8
Yi, B., Shen, X., Liu, H., Zhang, Z., Zhang, W., Liu, S., & Xiong, N. (2019). Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Transactions on Industrial Informatics, 15(8), 4591-4601. https://doi.org/10.1109/TII.2019.2893714
Zagranovskaia, A., & Mitura, D. (2021). Designing Hybrid recommender system, International Scientific and Practical Conference, pp. 1-5. https://doi.org/10.1145/3487757.3490921
Zagranovskaia, A., & Mitura, D. (2021). Designing hybrid recommender systems. In IV International Scientific and Practical Conference (pp. 1-5). https://doi.org/10.1145/3487757.3490921
Zhang, F., Gong, T., Lee, V. E., Zhao, G., Rong, C., & Qu, G. (2016). Fast algorithms to evaluate collaborative filtering recommender systems. Knowledge-Based Systems, 96, 96-103. https://doi.org/10.1016/j.knosys.2015.12.025
Zhang, F., Lee, V. E., Jin, R., Garg, S., Choo, K. K. R., Maasberg, M., & Cheng, C. (2019). Privacy-aware smart city: A case study in collaborative filtering recommender systems. Journal of Parallel and Distributed Computing, 127, 145-159. https://doi.org/10.1016/j.jpdc.2017.12.015
Zhang, Y., Liu, X., Liu, W., & Zhu, C. (2016). Hybrid recommender system using semi-supervised clustering based on Gaussian mixture model. International Conference on Cyberworlds (CW) (pp. 155-158). IEEE. https://doi.org/10.1109/CW.2016.32
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