Abstract
In today’s information-rich society, the importance of recommender systems for matching items and customers is increasing day by day. The development of e-commerce sites and review sites has made it possible to access a large amount of product descriptions and user reviews, and it is believed that more advanced recommendation models can be proposed by efficiently utilizing this text information. ConvMF is the first model that integrates text and probabilistic matrix factorization(PMF) which is one of the matrix factorization methods. In this method, features are extracted from item text such as item descriptions using CNN architecture and integrated into PMF. However they focus only on the item text and not on the user factor. As a result, this method can not reflect user characteristics. Therefore, this paper proposes a new recommender system to extract both item and user features from item and user text using CNN and integrate them into matrix factorization.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Saga R, Duan Y (2018) Apparel goods recommender system based on image shape features extracted by a CNN. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 2365–2369
Saga R, Hayashi Y, Tsuji H (2008) Hotel recommender system based on user’s preference transition. In: 2008 IEEE International Conference on Systems, Man and Cybernetics, pp 2437–2442
Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. Proceedings of the 20th international conference on neural information processing systems, ser NIPS 07. Curran Associates Inc, pp 1257–1264
Shi Y, Larson M, Hanjalic A (2010) Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. Proceedings of the workshop on context-aware movie recommendation ser CAMRa 10. Association for Computing Machinery, pp 34–40. https://doi.org/10.1145/1869652.1869658
Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. Proceedings of the 10th ACM conference on recommender systems, ser RecSys 16. Association for Computing Machinery, Cham, pp 233–240. https://doi.org/10.1145/2959100.2959165
McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. Proceedings of the 7th ACM conference on recommender systems, ser RecSys 13. Association for Computing Machinery, Cham, pp 165–172. https://doi.org/10.1145/2507157.2507163
McAuley J, Targett C, Shi Q, van den Hengel A (2015) Image-based recommendations on styles and substitutes. Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ser SIGIR 15. Association for Computing Machinery, Cham, pp 43–52. https://doi.org/10.1145/2766462.2767755
Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization. Proceedings of the 17th ACM conference on information and knowledge management, ser CIKM 08. Association for Computing Machinery, Cham, pp 931–940. https://doi.org/10.1145/1458082.1458205
Wang X, Yang X, Guo L, Han Y, Liu F, Gao B (2019) Exploiting social review-enhanced convolutional matrix factorization for social recommendation. IEEE Access 7(82):826–837
Beel J, Langer S, Gipp B (2017) TF-IDuF: a novel term-weighting scheme for user modeling based on users personal document collections, pp 452–459. https://kops.uni-konstanz.de/handle/123456789/41879. Accessed 21 March 2018
Musto C, Semeraro G, Degemmis M, Lops P (2015) Word embedding techniques for content-based recommender systems: an empirical evaluation. In: RecSys Posters
Zhang J-D, Chow C-Y (2018) Sema: deeply learning semantic meanings and temporal dynamics for recommendations. IEEE Access 6(54):106–116
Wu C, Wu F, An M, Huang Y, Xie X (2019) Neural news recommendation with topic-aware news representation. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 1154–1159
Liu P, Du J, Xue Z, Li A (2022) Bi-convolution matrix factorization algorithm based on improved convmf. In: Zhang L, Yu W, Jiang H, Laili Y (eds) Intelligent networked things. Springer Nature, Singapore, pp 122–134
Rakuten group Inc (2021) Rakuten ichiba data. Informatics research data repository, national institute of informatics (dataset). Rakuten group Inc. https://doi.org/10.32130/idr.2.1
Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, pp 1532–1543
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was presented in part at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 25–27, 2023).
About this article
Cite this article
Tamada, T., Saga, R. Double-ConvMF: probabilistic matrix factorization with user and item characteristic text. Artif Life Robotics 29, 107–113 (2024). https://doi.org/10.1007/s10015-023-00924-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-023-00924-5