Design and Implementation of a Collaborative Filtering Algorithm Based on Deep Learning and Matrix Factorization
DOI:
https://doi.org/10.4108/eetsis.10935Keywords:
Collaborative filtering, Deep learning, Matrix factorization, Recommendation algorithm, Data sparsity, RecallAbstract
INTRODUCTION: Collaborative filtering (CF) algorithms based on deep learning and matrix factorization aim to learn deeper latent features of users and items from user ratings. However, such methods often face challenges due to the sparsity of rating data, which limits their recommendation performance.
OBJECTIVES: This study aims to design a hybrid recommendation algorithm named auto-encoder deep learning and matrix factorization (AED-MF), which integrates deep learning and matrix factorization to address the sparsity issue in rating data and improve the extraction of deeper latent features of users and items.
METHODS: The AED-MF algorithm combines an auto-encoder-based deep learning approach with matrix factorization to model both deeper hidden representations and nonlinear relationships between users and items. The methodology includes data downloading, mounting, cleaning, model training, and experimental evaluation, with matrix factorization applied to predict missing ratings in the rating matrix.
RESULTS: The proposed AED-MF algorithm demonstrated strong performance in key recommendation metrics, effectively learning deeper user and item representations and applying them to capture complex nonlinear relationships. It also reduced the impact of rating data sparsity while maintaining high recommendation accuracy.
CONCLUSION: The AED-MF recommendation algorithm successfully alleviates the problem of sparse rating data and enhances recommendation accuracy by leveraging the strengths of both deep learning and matrix factorization, offering an effective solution for improving collaborative filtering-based recommender systems.
References
[1] Guttentag D. Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Current issues in Tourism, 2015, 18(12): 1192-1217.
[2] Yang Z R, He Z Y, Wang C D, et al. Collaborative Meta-Path Modeling for Explainable Recommendation. IEEE Transactions on Computational Social Systems, 2023, pp. 1–11.
[3] Negroponte N. Being Digital. Knopf, 1995, pp. 26-33.
[4] Wang Y, Ma W, Zhang M, et al. A survey on the fairness of recommender systems[J]. ACM Transactions on Information Systems, 2023, 41(3): 1-43.
[5] Morales-Murillo V G, Pinto D, Perez-Tellez F, et al. A Transformer-Based Multi-Domain Recommender System for E-commerce[J]. International Journal of Combinatorial Optimization Problems and Informatics, 2024, 15(2): 95.
[6] Kuo R J, Li S S. Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review[J]. Applied Soft Computing, 2023, 135: 110038.
[7] Walek B, Fajmon P. A hybrid recommender system for an online store using a fuzzy expert system. Expert Systems with Applications, 2023, 212: 118565, 1-16.
[8] Lee Y H, Wei C P, Hu P J H, et al. Small clues tell: A collaborative expansion approach for effective content-based recommendations. Journal of Organizational Computing and Electronic Commerce, 2020, 30(2): 111-128.
[9] Ge S, Wu C, Wu F, et al. Graph enhanced representation learning for news recommendation[C]//Proceedings of the web conference 2020. 2020: 2863-2869.
[10] Yi J, Zhu Y, Xie J, et al. Cross-modal variational auto-encoder for content-based micro-video background music recommendation[J]. IEEE Transactions on Multimedia, 2021, 25: 515-528.
[11] Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992, 35(12): 61-70.
[12] Lee G Y, Tseng W P. An enhanced memory-based collaborative filtering approach for context-aware recommendation //Proceedings of the World Congress on Engineering. 2015, 1, pp. 198-202.
[13] Choi K, Suh Y. A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems, 2013, 37: 146-153.
[14] Ungar L H, Foster D P. Clustering methods for collaborative filtering//AAAI workshop on recommendation systems. 1998, 1: 114-129.
[15] Chien Y H, George E I. A bayesian model for collaborative filtering//AISTATS. 1999, pp. 1-6.
[16] Heckerman D, Chickering D M, Meek C, et al. Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 2000, 1(Oct): 49-75.
[17] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks//Proceedings of the fourth ACM conference on Recommender systems. 2010: 135-142.
[18] Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 2011: 448-456.
[19] Narducci F, Basile P, Musto C, et al. Concept-based item representations for a cross-lingual content-based recommendation process. Information Sciences, 2016, 374: 15-31.
[20] Natarajan S, Vairavasundaram S, Natarajan S, et al. Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data[J]. Expert Systems with Applications, 2020, 149: 113248.
[21] Chu H, Xing X, Meng Z, et al. Towards a deep learning autoencoder algorithm for collaborative filtering recommendation//2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2019: 239-243.
[22] Aäron Van Den Oord, Sander Dieleman, and Benjamin Schrauwen. Deep content-based music recommendation. In NIPS, 2013, pages 2643–2651.
[23] Zhao W, Ma H, Li Z, et al. Improving social and behavior recommendations via network embedding. Information Sciences, 2020, 516: 125-141.
[24] Majumdar A. Blind denoising autoencoder. IEEE transactions on neural networks and learning systems, 2018, 30(1): 312-317.
[25] Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations. Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198.
[26] Khan M M, Ibrahim R, Ghani I. Cross domain recommender systems: A systematic literature review[J]. ACM Computing Surveys (CSUR), 2017, 50(3): 1-34.
[27] Zhu F, Wang Y, Chen C, et al. Cross-domain recommendation: challenges, progress, and prospects[J]. arXiv preprint arXiv:2103.01696, 2021.
[28] Li P, Tuzhilin A. Ddtcdr: Deep dual transfer cross domain recommendation[C]//Proceedings of the 13th international conference on web search and data mining. 2020: 331-339.
[29] Zhao C, Zhao H, He M, et al. Cross-domain recommendation via user interest alignment[C]//Proceedings of the ACM web conference 2023. 2023: 887-896.
[30] Xu Z, Chen S, Pan W, et al. A multi-view graph contrastive learning framework for cross-domain sequential recommendation[J]. ACM Transactions on Recommender Systems, 2025, 3(4): 1-28.
[31] Panagiotakis C, Papadakis H, Papagrigoriou A, et al. Improving recommender systems via a dual training error based correction approach[J]. Expert Systems with Applications, 2021, 183: 115386.
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