User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

Widiyaningtyas T., Hidayah I., Adji T.B.

Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia; Department of Electrical Engineering, Universitas Negeri Malang, Malang, Indonesia


Abstract

Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%. © 2021, The Author(s).

Collaborative filtering; UPCSim; User behavior value; User rating value


Journal

Journal of Big Data

Publisher: Springer Science and Business Media Deutschland GmbH

Volume 8, Issue 1, Art No 52, Page – , Page Count


Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103547164&doi=10.1186%2fs40537-021-00425-x&partnerID=40&md5=51dd798b34e61cc2cf867fe23f115862

doi: 10.1186/s40537-021-00425-x

Issn: 21961115

Type: All Open Access, Gold


References

Xu, G., Tang, Z., Ma, C., Liu, Y., Daneshmand, M., A collaborative filtering recommendation algorithm based on user confidence and time context (2019) J Electr Comput Eng, 2019, pp. 1-12; Feng, J., Fengs, X., Zhang, N., Peng, J., An improved collaborative filtering method based on similarity (2018) PLoS ONE, 13 (9), pp. 1-18; Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X., A new user similarity model to improve the accuracy of collaborative filtering (2014) Knowl Based Syst, 56, pp. 156-166; Camacho, L.A., Alves-Souza, S.N., Social network data to alleviate cold-start in recommender system: a systematic review (2018) Inf Process Manag, 54, pp. 529-544; Sahu, A.K., Dwivedi, P., User profile as a bridge in cross-domain recommender systems for sparsity reduction (2019) Appl Intell, 49, pp. 2461-2481; Kumar, P., Kumar, V., Thakur, R.S., A new approach for rating prediction system using collaborative filtering (2019) Iran J Comput Sci, 2, pp. 81-87; Alonso, S., Bobadilla, J., Ortega, F., Moya, R., Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems (2019) IEEE Access, 7, pp. 41782-41798; Salah, A., Rogovschi, N., Nadif, M., A dynamic collaborative filtering system via a weighted clustering approach (2015) Neurocomputing, 175, pp. 206-215; Aggarwal, C.C., (2016) Recommender systems, , Springer, New York; Zhang, J., Lin, Y., Lin, M., Liu, J., An effective collaborative filtering algorithm based on user preference clustering (2016) Appl Intell, 45, pp. 230-240; Laishram, A., Padmanabhan, V., Lal, R.P., Analysis of similarity measures in user-item subgroup based collaborative filtering via genetic algorithm (2018) Int J Inf Technol, 10 (4), pp. 523-527; Bagher, R., Cami Hassanpour, H., Mashayekhi, H., User trends modeling for a content-based recommender system (2017) Expert Syst Appl, 87, pp. 209-219; Li, G., Zhang, Z., Wang, L., Chen, Q., Pan, J., One-class collaborative filtering based on rating prediction and ranking prediction (2017) Knowl Based Syst, 124, pp. 46-54; Wang, S., Huang, S., Liu, T.-Y., Ma, J., Chen, Z., Veijalainen, J., Ranking-oriented collaborative filtering: a listwise approach (2016) ACM Trans Inf Syst, 35 (2), pp. 1-28; Karabadji, N.E.I., Beldjoudi, S., Seridi, H., Aridhi, S., Dhifli, W., Improving memory-based user collaborative filtering with evolutionary multi-objective optimization (2018) Expert Syst Appl, 98, pp. 153-165; Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S., A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data (2015) Knowl Based Syst, 82, pp. 163-177; Ocepeka, U., Rugeljb, J., Bosnića, Z., Improving matrix factorization recommendations for examples in cold start (2015) Expert Syst Appl, 42 (19), pp. 6784-6794; Tran, C., Kim, J.Y., Shin, W.Y., Kim, S.W., Clustering-based collaborative filtering using an incentivized/penalized user model (2019) IEEE Access, 7, pp. 62115-62125; Bobadilla, J., Bojorque, R., Esteban, A.H., Hurtado, R., Recommender systems clustering using Bayesian non negative matrix factorization (2018) IEEE Access, 6, pp. 3549-3564; Vander Aa, T., Chakroun, I., Haber, T., Distributed Bayesian probabilistic matrix factorization (2017) Procedia of International Conference on Computational Science, ICCS, 12–14 June 2017, Zurich, Switzerland, pp. 1030-1039. , https://doi.org/10.1016/j.procs.2017.05.009; Zhang, R., Mao, Y., Movie recommendation via Markovian factorization of matrix processes (2019) IEEE Access, 7, pp. 13189-13199; Xian, Z., Li, Q., Li, G., Li, L., New collaborative filtering algorithms based on SVD++ and differential privacy (2017) Math Probl Eng, 2017, pp. 1-14; Guan, X., Li, C.T., Guan, Y., Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems (2017) IEEE Access, 5, pp. 27668-27678; Kherad, M., Bidgoly, A.J., (2020) Recommendation system using a deep learning and graph analysis approach, pp. 1-11; Li, Z., Chen, H., Lin, K., Shakhov, V., Shi, L., Double attention-based deformable convolutional network for recommendation Proceedings of the 2020 IEEE/CIC International Conference on Communications in China (ICCC); 2020, pp. 1051-1056. , https://doi.org/10.1109/ICCC49849.2020.9238819; Yue, L., Sun, X.X., Gao, W.Z., Feng, G.Z., Zhang, B.Z., Multiple auxiliary information based deep model for collaborative filtering (2018) J Comput Sci Technol, 33 (4), pp. 668-681; Shams, B., Haratizadeh, S., Item-based collaborative ranking (2018) Knowl Based Syst J, 152, pp. 172-185; Park, Y., Park, S., Jung, W., Lee, S.G., Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph (2015) Expert Syst Appl, 42 (8), pp. 4022-4028; Polatidis, N., Georgiadis, C.K., A multi-level collaborative filtering method that improves recommendations (2016) Expert Syst Appl, 48, pp. 100-110; Sun, S.B., Zhang, Z.H., Dong, X.L., Zhang, H.R., Li, T.J., Zhang, L., Min, F., Integrating triangle and Jaccard similarities for recommendation (2017) PLoS ONE, 12 (8), pp. 1-11; Wu, C., Wu, J., Luo, C., Wu, Q., Liu, C., Wu, Y., Yang, F., Recommendation algorithm based on user score probability and project type (2019) Eurasip J Wirel Commun Netw, 2019 (80), pp. 1-13; Proceedings of 2015 4th international conference on computer science and network technology ICCSNT 2015, 2015, pp. 239-243. , https://doi.org/10.1109/ICCSNT.2015.7490744; Al-Shamri, M.Y.H., User profiling approaches for demographic recommender systems (2016) Knowl Based Syst, 100, pp. 175-187; Yassine, A., Mohamed, L., Al Achhab, M., Intelligent recommender system based on unsupervised machine learning and demographic attributes (2020) Simul Model Pract Theory, 107, pp. 1-9; Harper, F.M., Konstan, J.A., The movielens datasets: history and context (2015) ACM Trans Interact Intell Syst, 5 (4), pp. 1-19; Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., Salehi, M., Evaluating collaborative filtering recommender algorithms: a survey (2018) IEEE Access, 6, pp. 74003-74024; Zhang, F., Gong, T., Lee, V.E., Zhao, G., Rong, C., Qu, G., Fast algorithms to evaluate collaborative filtering recommender systems (2016) Knowl Based Syst, 96, pp. 96-103; Zheng, M., Min, F., Zhang, H.R., Chen, W.B., Fast recommendations with the m-distance (2016) IEEE Access, 4, pp. 1464-1468; Vellaichamy, V., Kalimuthu, V., Hybrid collaborative movie recommender system using clustering and bat optimization (2017) Int J Intell Eng Syst, 10 (5), pp. 38-47; Fan, X., Chen, Z., Zhu, L., Liao, Z., Fu, B., A novel hybrid similarity calculation model (2017) Sci Program, 2017, pp. 1-9; Bansal, S., Baliyan, N., A study of recent recommender system techniques (2019) Int J Knowl Syst Sci, 10 (2), pp. 13-41; Solomatine, D.P., Ostfeld, A., Data-driven modelling: some past experiences and new approaches (2008) J Hydroinform, 10 (1), pp. 3-22; Buchadas, A., Vas, A.S., Honrado, J.P., Alagador, D., Bastos, R., Cabral, J.A., Santos, M., Vicente, J.R., Dynamic models in research and management of biological invasions (2017) J Environ Manag, 196, pp. 594-606

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