Abstract
Today, several successful companies like Uber, Airbnb, and others have adopted sharing economy business models. The increasing growth of websites and applications adopting this model pushes companies to develop differentiation strategies. One of the strategies is to use emerging technologies to offer a better customer experience. Recommender systems (RSs) are AI-based solutions that can provide customized recommendations. To implement an RS in a sharing economy platform, this study intends to compare the performance of two recommendation-system approaches based on their accuracy, computation time, and scalability. The Netflix dataset was used to compare matrix factorization and memory-based techniques based on their performances using offline testing. The results of the study indicate that memory-based methods are more accurate for small datasets but have computation time limitations for large datasets. Single-value decomposition methods scale better than memory-based algorithms.
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References
Puschmann, T., Alt, R.: Sharing economy. Bus. Inf. Syst. Eng. 58(1), 93–99 (2016). https://doi.org/10.1007/s12599-015-0420-2
Ricci, F., Rokach, L., Shapira, B.: Recommender systems: techniques, applications, and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, 3rd edn., pp. 1–35. Springer, New York (2022)
Kim, S., Yoon, Y.: Recommendation system for sharing economy based on multidimensional trust model. Multim. Tools Appl. 75(23), 15297–15310 (2016). https://doi.org/10.1007/s11042-014-2384-5
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Shapira, B. Ricci, F., Rokach, L., Kantor, P.B. (eds.) Recommender systems handbook, 1st edn., pp. 1–35. Springer, New York (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.:. Recommender System Application Developments: A Survey (n.d.)
Ren, J., et al.: Matching algorithms: fundamentals, applications and challenges.arXiv preprint arXiv:2103.03770 (2021)
Raghuwanshi, S.K., Pateriya, R.K.: Recommendation systems: techniques, challenges, application, and evaluation. In: Bansal, J.C., Das, K.N., Nagar, A., Deep, K., Ojha, A.K. (eds.) Soft Computing for Problem Solving, vol. 817, pp. 151–164. Springer Singapore (2019). https://doi.org/10.1007/978-981-13-1595-4_12
Lops, P., Gemmis, M.D., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Shapira, B. Ricci, F., Rokach, L., Kantor, P.B. (eds.) Recommender Systems Handbook, 1st edn, pp. 73–105. Springer, New York (2011)
Shah, S.H., Duni, F.: A review on matrix factorization techniques used for an intelligent recommender system. Turkish J. Comput. Math. Educ. 12(7), 1812–1823 (2021)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 1–19 (2009). https://doi.org/10.1155/2009/42142
Kluver, D., Ekstrand, M.D., Konstan, J.A.: Rating-based collaborative filtering: algorithms and evaluation. In: Brusilovsky, P., He, D. (eds.) Social Information Access, vol. 10100, pp. 344–390. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90092-6_10
Wu, L., He, X., Wang, X., Zhang, K., Wang, M.: A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans. Knowl. Data Eng. 35(5), 4425–4445 (2022). https://doi.org/10.1109/TKDE.2022.3145690
Hanafi, M., Suryana, N., Basari, A.S.H.: An understanding and approach solution for cold start problem associated with recommender system: a literature review 96(9), 2677–2695 (2005)
Do, M.-P., Nguyen, D.V., Nguyen, L.: Model-based approach for collaborative filtering [Paper presentation]. In: The 6th International Conference on Information Technology for Education, Ho Chi Minh City (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). https://doi.org/10.1109/MC.2009.263
Adomavicius, G., Tuzhilin, A. (s.d.): Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99
Mu, R.: A survey of recommender systems based on deep learning. IEEE Access 6, 69009–69022 (2018). https://doi.org/10.1109/ACCESS.2018.2880197
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 1–38 (2020). https://doi.org/10.1145/3285029
Batmaz, Z., Yurekli, A., Bilge, A., Kaleli, C.: A review on deep learning for recommender systems: challenges and remedies. Artif. Intell. Rev. 52(1), 1–37 (2019). https://doi.org/10.1007/s10462-018-9654-y
Najafabadi, M.K., Mohamed, A., Onn, C.W.: An impact of time and item influencer in collaborative filtering recommendations using graph-based model. Inf. Process. Manage. 56(3), 526–540 (2019). https://doi.org/10.1016/j.ipm.2018.12.007
Zagranovskaia, A.V., Mitiura, D.Yu., Makarchuk, T.A.: Designing a digital content recommendation system for films. In: Nazarov, A.D. (ed.) Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: From Regional Development To Global Economic Growth” (MTDE 2020), Atlantis Press, Yekaterinburg (2020). https://doi.org/10.2991/aebmr.k.200502.001
Ghazanfar, M.A., Prügel-Bennett, A.: Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst. Appl. 41(7), 3261–3275 (2014). https://doi.org/10.1016/j.eswa.2013.11.010
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Shapira, B. Ricci, F., Rokach, L., Kantor, P.B. (eds.) Recommender Systems Handbook, 1st edn, pp. 257–297. Springer, New York (2011). https://doi.org/10.1007/978-0-387-85820-3_8
Kuanr, M., Mohapatra, P.: Assessment methods for evaluation of recommender systems: a survey. Found. Comput. Decis. Sci. 46(4), 393–421 (2021). https://doi.org/10.2478/fcds-2021-0023
Bennett, J., Lanning, S.: The Netflix Prize. 2007, 35 (2007)
Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 273–282 (2014). https://doi.org/10.1145/2556195.2556248
Gunawardana, A., Shani, G., Yogev, S.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, 3rd edn., pp. 547–601. Springer, New York (2022)
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Seridi, K., El Rharras, A. (2024). A Comparative Analysis of Memory-Based and Model-Based Collaborative Filtering on Recommender System Implementation. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_7
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