Abstract
In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender systems. In this paper, we propose an extended matrix factorization model based on network representation learning. To characterize users and items comprehensively, we construct the user relation network and the item relation network from the multi-source data. Then the representation vectors of users and items are learned from two networks respectively. The representation vectors learned from the relation networks can characterize users and items more effectively. Since users and items belong to different vector spaces, a matrix is used to connect user and item representation vectors when predicting ratings. To obtain the connection matrix, stochastic gradient descent is applied to minimize the errors between the predicted and observed ratings. Experimental results on two real-world datasets, Yelp and Douban, demonstrate the effectiveness of our model compared to the state-of-the-art recommendation algorithms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, ACM, Boston, MA, USA, DLRS 2016, pp 7–10
Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Halifax, NS, Canada, pp 135–144
Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y (2019) Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Anchorage, AK, USA, pp 2478–2486
Fu Ty, Lee WC, Lei Z (2017) Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on conference on information and knowledge management, ACM, Singapore, Singapore, pp 1797–1806
Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Francisco, California USA, pp 855–864
Hu L, Xu S, Li C, Yang C, Shi C, Duan N, Xie X, Zhou M (2020) Graph neural news recommendation with unsupervised preference disentanglement. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL, Online, pp 4255–4264
Hussein R, Yang D, Cudré-Mauroux P (2018) Are meta-paths necessary? revisiting heterogeneous graph embeddings. In: Proceedings of the 27th ACM international conference on information and knowledge management. ACM, Torino, Italy, pp 437–446
Ji S, Feng Y, Ji R, Zhao X, Tang W, Gao Y (2020) Dual channel hypergraph collaborative filtering. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA, pp 2020–2029
Jin B, Cheng K, Zhang L, Fu Y, Yin M, Jiang L (2020) Partial relationship aware influence diffusion via a multi-channel encoding scheme for social recommendation. In: Proceedings of the 29th ACM international conference on information and knowledge management. ACM, Virtual Event, Ireland, pp 585–594
Konstan JA, Miller BN, Maltz DA, Herlocker JL, Gordon LR, Riedl J (1997) Grouplens. Commun ACM 40(3):77–87
Koren Y, Bell R (2011) Advances in collaborative filtering. Springer, Boston, MA, pp 145–186
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Lee J, Kim S, Lebanon G, Singer Y, Bengio S (2016) Llorma: local low-rank matrix approximation. J Mach Learn Res 17(1):442–465
Levy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. Advances in neural information processing systems. MIT Press, Montreal, pp 2177–2185
Ling G, Lyu M, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM Conference on Recommender Systems, ACM, Foster City, Silicon Valley California, USA, pp 105–112
Liu H, Jiang Z, Song Y, Zhang T, Wu Z (2019) User preference modeling based on meta paths and diversity regularization in heterogeneous information networks. Knowl-Based Syst 181:104784
Luo C, Pang W, Wang Z, Lin C (2014) Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations. In: Proceedings of the 14th IEEE international conference on data mining series, IEEE, Shenzhen, China, pp 917–922
Ma H, King I, Lyu M (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, ACM, Boston, MA, USA, pp 203–210
Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on web search and data mining, ACM, Hong Kong, China, pp 287–296
Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. Advances in neural information processing systems. MIT Press, Vancouver, pp 1257–1264
Myo T, How-Lung E, Remagnino P (2012) Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Trans Cybern 43(6):2147–2156
Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, ACM, San Jose, California, USA 2007:5–8
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, pp 701–710
Qiu J, Dong Y, Ma H, Li J, Wang K, Tang J (2018) Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec. In: Proceedings of the eleventh ACM international conference on web search and data mining. ACM, Marina Del Rey, CA, USA, pp 459–467
Sarwar B (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, association for computing machinery, New York, NY, USA, pp 285–295
Shi C, Zhang Z, Luo P, Yu PS, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, Melbourne, Australia, pp 453–462
Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37
Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370
Sun Y, Han J (2013) Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explor Newsl 14(2):20–28
Sun Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endowment 4(11):992–1003
Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide weeb, international world wide web conferences steering committee, Florence, Italy, pp 1067–1077
Wang Z, Liu H, Du Y, Wu Z, Zhang X (2019) Unified embedding model over heterogeneous information network for personalized recommendation. Twenty-Eighth international joint conference on artificial intelligence IJCAI-19. Morgan Kaufmann, Macao, China, pp 3813–3819
Wang X, Wang Y, Ling Y (2020) Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation. In: Proceedings of the AAAI conference on artificial intelligence, New York, NY, USA, pp 6275–6282
Wen Y, Guo L, Chen Z, Ma J (2018) Network embedding based recommendation method in social networks. In: Companion proceedings of the web conference 2018, international world wide web conferences steering committee, Republic and Canton of Geneva, CHE, pp 11–12
Xu Y, Zhu Y, Shen Y, Yu J (2019) Learning shared vertex representation in heterogeneous graphs with convolutional networks for recommendation. In: Proceedings of the Twenty-Eighth international joint conference on artificial intelligence, international joint conferences on artificial intelligence organization, Republic and Canton of Geneva, CHE, pp 4620–4626
Yu J, Gao M, Li J, Yin H, Liu H (2018) Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, ACM, Torino, Italy, pp 357–366
Yu X, Ren X, Gu Q, Sun Y, Han J (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks. IJCAI HINA 27
Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on web search and data mining. ACM, New York, NY, USA, pp 283–292
Zhang Y, Ai Q, Chen X, Croft W (2017) Joint representation learning for top-n recommendation with heterogenous information sources. ACM conference on information and knowledge management. ACM, Singapore, Singapore, pp 1449–1458
Zhao H, Yao Q, Li J, Song Y, Lee DL (2017) Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Halifax, NS, Canada, pp 635–644
Zhao Z, Zhang X, Zhou H, Li C, Gong M (2020) Hetnerec: heterogeneous network embedding based recommendation. Knowl-Based Syst 204(11):106218. https://doi.org/10.1016/j.knosys.2020.106218
Acknowledgements
Thanks to Professor Fenlin Liu and Dr. Daofu Gong for their guidance on this article. Thanks to Dr. Lei Tan for his suggestions on the revision of this article.
Funding
This work was supported in part by the National Natural Science Foundation of China (U1804263).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, J., Tan, L., Gong, D. et al. Extended matrix factorization with entity network construction for recommendation. J Ambient Intell Human Comput 13, 1763–1775 (2022). https://doi.org/10.1007/s12652-021-03345-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03345-z