Abstract
Recommendation techniques play a vital role in recommending an actual product to an intended user. The recommendation also supports the user in the decision-making process. In recent years, collaborative filtering has been a widely used technique in recommender systems. A model-based CF technique called matrix factorization fills the user–item interaction matrix’s missing elements. One of the significant challenges in MF is filling those elements in a row or column. The user has a few observations about an item, leading to sparsity issues of collaborative filtering. Therefore, conventional MF alone is not suitable to address the new item or user problem. We propose an MF model that integrates user-side information to handle these issues. We integrate user-side information in terms of vectors and bias to overcome the sparsity problem of collaborative filtering. We exhaustively evaluate our model on real-world datasets for predicting the ratings. The experiment results and analysis demonstrate that the proposed approach improves predictions significantly compared to the state-of-the-art techniques.
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References
Aljunid, M.F., Dh, M.: An efficient deep learning approach for collaborative filtering recommender system. Proc. Comput. Sci. 171, 829–836 (2020)
Angulo, C., Falomir, Z., Anguita, D., Agell, N., Cambria, E.: Bridging cognitive models and recommender systems. Cogn. Comput. 12, 426–7 (2020)
Ar, Y.: An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets. Evol. Intell. 13(2), 269–81 (2019)
Behara, G., Yannam, V.R., Nayyar, A., Bagal, D.K.: Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender system. Multimed. Tools Appl. 1–23 (2023)
Behera, G., Mohapatra, R.K., Bhoi, A.K.: A mixed collaborative recommender system using singular value decomposition and item similarity. In: International Conference on Machine Learning, IoT and Big Data, pp. 259–267. Springer (2023)
Behera, G., Nain, N.: A comparative study of big mart sales prediction. In: International Conference on Computer Vision and Image Processing, pp. 421–432. Springer (2019)
Behera, G., Nain, N.: Grid search optimization (gso) based future sales prediction for big mart. In: 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 172–178. IEEE (2019)
Behera, G., Nain, N.: Collaborative recommender system (crs) using optimized sgd-als. In: International Conference on Advances in Computing and Data Sciences, pp. 627–637. Springer (2021)
Behera, G., Nain, N.: Handling data sparsity via item metadata embedding into deep collaborative recommender system. J. King Saud Univ. Comput. Inf. Sci. 34(10), 9953–63 (2022)
Behera, G., Nain, N.: Trade-off between memory and model-based collaborative filtering recommender system. In: Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences, pp. 137–146. Springer (2022)
Behera, G., Nain, N.: Collaborative filtering with temporal features for movie recommendation system. Proc. Comput. Sci. 218, 1366–1373 (2023)
Behera, G., Nain, N.: The state-of-the-art and challenges on recommendation system’s: principle, techniques and evaluation strategy. SN Comput. Sci. 4(5), 677 (2023)
Bennett, J., Lanning, S., et al.: The netflix prize. In: Proceedings of KDD cup and workshop, p. 35. Citeseer (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Davagdorj, K., Park, K.H., Ryu, K.H.: A collaborative filtering recommendation system for rating prediction. In: Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 265–271. Springer (2020)
Grčar, M., Mladenič, D., Fortuna, B., Grobelnik, M.: Data sparsity issues in the collaborative filtering framework. In: International Workshop on Knowledge Discovery on the Web, pp. 58–76. Springer (2005)
Guan, X., Li, C.T., Guan, Y.: Matrix factorization with rating completion: an enhanced svd model for collaborative filtering recommender systems. IEEE Access 5, 27668–27678 (2017)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)
Hastie, T., Mazumder, R., Lee, J.D., Zadeh, R.: Matrix completion and low-rank svd via fast alternating least squares. J. Mach. Learn. Res. 16(1), 3367–3402 (2015)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–558 (2016)
Hernando, A., Bobadilla, J., Ortega, F.: A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl. Based Syst. 97, 188–202 (2016)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57 (1999)
Huang, L., Tan, W., Sun, Y.: Collaborative recommendation algorithm based on probabilistic matrix factorization in probabilistic latent semantic analysis. Multimed. Tools Appl. 78(7), 8711–8722 (2019)
Jenatton, R., Le Roux, N., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), pp. 3176–3184 (2012)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv:1412.6980
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Li, H., Diao, X., Cao, J., Zheng, Q.: Collaborative filtering recommendation based on all-weighted matrix factorization and fast optimization. IEEE Access 6, 25248–25260 (2018)
Li, H., Li, K., An, J., Li, K.: Msgd: a novel matrix factorization approach for large-scale collaborative filtering recommender systems on gpus. IEEE Trans. Parallel Distrib. Syst. 29(7), 1530–1544 (2017)
Lin, W., Leng, H., Dou, R., Qi, L., Pan, Z., Rahman, M.A.: A federated collaborative recommendation model for privacy-preserving distributed recommender applications based on microservice framework. J. Parallel Distrib. Comput. 174, 70–80 (2023)
Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Ind. Inf. 10(2), 1273–1284 (2014)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940 (2008)
Mehta, R., Rana, K.: A review on matrix factorization techniques in recommender systems. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 269–274. IEEE (2017)
Meng, J., Zheng, Z., Tao, G., Liu, X.: User-specific rating prediction for mobile applications via weight-based matrix factorization. In: 2016 IEEE International Conference on Web Services (ICWS), pp. 728–731. IEEE (2016)
Meng, S., Gao, Z., Li, Q., Wang, H., Dai, H.N., Qi, L.: Security-driven hybrid collaborative recommendation method for cloud-based iot services. Comput. Secur. 97, 101950 (2020)
Mohamed, M.H., Khafagy, M.H., Ibrahim, M.H.: Recommender systems challenges and solutions survey. In: 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 149–155. IEEE (2019)
Mohammadi, M., Naree, S.A., Lati, M.: User-item content awareness in matrix factorization based collaborative recommender systems. Intell. Data Anal. 24(3), 723–739 (2020)
Nassar, N., Jafar, A., Rahhal, Y.: Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization. J. Big Data 7, 1–12 (2020)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, pp. 5–8 (2007)
Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl.-Based Syst. 82, 163–177 (2015)
Porteous, I., Bart, E., Welling, M.: Multi-hdp: a non parametric Bayesian model for tensor factorization. In: AAAI, vol. 8, pp. 1487–1490 (2008)
Ranjbar, M., Moradi, P., Azami, M., Jalili, M.: An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Eng. Appl. Artif. Intell. 46, 58–66 (2015)
Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Recommender Systems Handbook, pp. 1–34. Springer (2015)
Rowe, M.: Semanticsvd++: incorporating semantic taste evolution for predicting ratings. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 213–220. IEEE (2014)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887 (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science, vol. 1, pp. 27–8. Citeseer (2002)
Soares, M., Viana, P.: Tuning metadata for better movie content-based recommendation systems. Multimed. Tools Appl. 74(17), 7015–7036 (2015)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)
Wang, F., Zhu, H., Srivastava, G., Li, S., Khosravi, M.R., Qi, L.: Robust collaborative filtering recommendation with user-item-trust records. IEEE Trans. Comput. Soc. Syst. 9(4), 986–996 (2021)
Wang, R., Cheng, H.K., Jiang, Y., Lou, J.: A novel matrix factorization model for recommendation with lod-based semantic similarity measure. Expert Syst. Appl. 123, 70–81 (2019)
Wang, Y.X., Zhang, Y.J.: Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336–1353 (2012)
Wu, Z., Tian, H., Zhu, X., Wang, S.: Optimization matrix factorization recommendation algorithm based on rating centrality. In: International Conference on Data Mining and Big Data, pp. 114–125. Springer (2018)
Yu, H., Sun, L., Zhang, F.: A robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems based on user anomaly rating behavior detection. KSII Trans. Internet Inf. Syst. 13(9) (2019)
Zhang, S., Yao, L., Xu, X.: Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 957–960 (2017)
Acknowledgements
The authors would like to thank the Department of CSE, MNIT Jaipur, for providing the resources during the experiment.
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GB conceptualization, data curation, formal analysis, methodology, resources, software, validation, visualization, and writing an original draft. NN formal analysis, methodology, supervision, validation, and writing—review and editing. RKS formal analysis, data curation, validation, and methodology. All authors reviewed the manuscript.
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Behera, G., Nain, N. & Soni, R.K. Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering. Multimedia Systems 30, 64 (2024). https://doi.org/10.1007/s00530-024-01261-8
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DOI: https://doi.org/10.1007/s00530-024-01261-8