Abstract
Traditional recommendation systems only consider the content of users to predict the rating of items in the recommendation process, and ignore the impact of other factors on the recommendation process except for the user-item rating matrix. Recommendation models that are based on item attributes can portray user preferences and item characteristics from item attribute information, which alleviates the sparseness of rating data to a certain extent. However, they do not consider the potential factors of users and items in the rating matrix. The advantage of deep learning methods in feature representation and feature learning is that they can extract the deep sublinear features of users and items contained in the rating matrix and item attributes. To further improve the quality of recommendation, we proposes the genre rate neural network recommendation (GRNNRec) model, which integrates item attributes based on deep learning. This model integrates the user-item rating matrix and item attributes into a deep neural network to characterize the performance of both in low-dimensional space. First, we use static coding to characterize the properties of the items. Second, we use feature mapping and feature concatenation methods to learn the higher-order features of both continuously. Finally, through the periodic learning rate and the decay rate, we achieve rating prediction. The experiments on different recommendation datasets demonstrate that our model can significantly improve the accuracy of rating prediction in recommendation systems.
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
Ahn, H.J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37–51.
Almahairi, A., Kastner, K., Cho, K., & Courville, A. (2015). Learning distributed representations from reviews for collaborative filtering. In Proceedings of the 9th ACM Conference on Recommender Systems, (pp. 147–154).
Anwaar, F., Iltaf, N., Afzal, H., & Nawaz, R. (2018). HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items. Journal of Computational Science, 29, 9–18.
Bao, Y., Fang, H., & Zhang, J. (2014). TopicMF: Simultaneously exploiting ratings and reviews for recommendation. In Proceedings of the national conference on artificial intelligence (pp. 2–8).
Bell, R., Koren, Y., & Volinsky, C. (2007). Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. (95–104).
Chen, R., Hua, Q., Chang, Y., Wang, B., Zhang, L., & Kong, X. (2018). A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, 6, 64301–64320.
Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on machine learning. (pp. 160–167).
Dauphin, Y.N., Vries, H., Chung, J., & Bengio, Y. (2015). RMSProp and equilibrated adaptive learning rates for non-convex optimization. In Proceedings of the 28th international conference on neural information processing systems. (pp. 1504–1512).
de Gemmis, M., Lops, P., Musto, C., Narducci, F., & Semeraro, G. (2015). Semantics-aware content-based recommender systems. In Recommender systems handbook (pp. 119–159). Boston: Springer.
Dziugaite, G.K., & Roy, D.M. (2015). Neural network matrix factorization. arXiv:1511.06443.
Georgiev, K., & Nakov, P. (2013). A non–IID framework for collaborative filtering with restricted Boltzmann machines. In Proceedings of the 30th international conference on machine learning, (Vol. 28 pp. III–1148–III–1156).
Goldberg, D., Nichols, D., Oki, B.M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70.
Guo, G., Zhang, J., & Yorke-Smith, N. (2015). TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the twenty-ninth AAAI conference on artificial intelligence. (pp. 123–129).
Guo, L., Liang, J., Zhu, Y., Luo, Y., Sun, L., & Zheng, X. (2019). Collaborative filtering recommendation based on trust and emotion. Intelligent Information Systems, 53(1), 113–135.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition. (pp. 770–778).
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.S. (2017). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. (pp. 173–182).
Herlocker, J.L., Konstan, J.A., Terveen, L.G., & Riedl, J.T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.
Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980.
Kiran, R., Kumar, P., & Bhasker, B. (2020). DNNRec: A novel deep learning based hybrid recommender system. Expert Systems with Applications, 144, 1–14.
Koren, Y. (2008). 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).
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Lee, D.D., & Seung, H.S. (2000). Algorithms for non-negative matrix factorization. In Proceedings of advances in neural information processing systems. (pp. 556–562).
Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W. (2003). Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. (pp. 345–354).
Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.
Musto, C., Franza, T., Semeraro, G., Gemmis, M., & Lops, P. (2018). Deep content-based recommender systems exploiting recurrent neural networks and linked open data. In Adjunct Publication of the 26th conference on user modeling, adaptation and personalization. (pp. 239–244).
Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop. (pp. 39–42).
Qiu, L., Gao, S., Cheng, W., & Guo, J. (2016). Aspect-based latent factor model by integrating ratings and reviews for recommender system. Knowledge Based Systems, 110, 233–243.
Rutkowski, A.F., & Saunders, C.S. (2010). Growing pains with information overload. Computer, 43(6), 95–96.
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on machine learning. (pp. 791–798).
Salakhutdinov, R., & Mnih, A. (2007). Probabilistic matrix factorization. In Proceedings of the 20th international conference on neural information processing systems. (pp. 1257–1264).
Santana, I.A.P., & Domingues, M.A. (2020). A systematic review on context-aware recommender systems using deep learning and embeddings. arXiv:2007.04782.
Sedhain, S., Menon, A.K., Sanner, S., & Xie, L. (2015). AutoRec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on world wide web. (pp. 111–112).
Shokeen, J., & Rana, C. (2020). Social recommender systems: techniques, domains, metrics, datasets and future scope. Intelligent Information Systems, 54 (3), 633–667.
Strub, F., & Mary, J. (2015). Collaborative filtering with stacked denoising autoencoders and sparse inputs. In Nips workshop on machine learning for ecommerce.
Suglia, A., Greco, C., Musto, C., Gemmis, M., Lops, P., & Semeraro, G. (2017). A deep architecture for content-based recommendations exploiting recurrent neural networks. In Proceedings of the 25th conference on user modeling, adaptation and personalization. (pp. 202–211).
Sutskever, I., Martens, J., Dahl, G., & Hinton, G. (2013). On the importance of initialization and momentum in deep learning. In Proceedings of the 30th international conference on international conference on machine learning, (Vol. 38 pp. III–1139–III–1147).
Tarnowska, K., Ras, Z.W., & Daniel, L. (2020). Recommender system for improving customer loyalty, studies in big data Vol. 55. Berlin: Springer.
Tsuruoka, Y., Tsujii, J., & Ananiadou, S. (2009). Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In Proceedings of the 47th annual meeting of the association for computational linguistics. (pp. 477–485).
Wang, H., Wang, N., & Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. (pp. 1235–1244).
Wang, Q., Zhao, W., Yang, J., Wu, J., Hu, W., & Xing, Q. (2019). Deeptrust: A deep user model of homophily effect for trust prediction. In 2019 IEEE international conference on data mining. (pp. 618–627).
Wang, Y., Deng, J., Gao, J., & Zhang, P. (2017). A hybrid user similarity model for collaborative filtering. Information Sciences, 418-419, 102–118.
Wu, Y., DuBois, C., Zheng, A.X., & Ester, M. (2016). Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the ninth ACM international conference on web search and data mining. (pp. 153–162).
Xin, D., Lei, Y., Wu, Z., Sun, Y., Yuan, L., & Zhang, F. (2017). A hybrid collaborative filtering model with deep structure for recommender systems. In Proceedings of the 31th AAAI conference on artificial intelligence. (pp. 1309–1315).
Xue, H., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017). Deep matrix factorization models for recommender systems. In Proceedings of the 26th international joint conference on artificial intelligence. (pp. 3203–3209).
Yang, R., Feng, L., Wang, H., Yao, J., & Luo, S. (2020). Parallel recurrent convolutional neural networks-based music genre classification method for mobile devices. IEEE Access, 8, 19629–19637.
Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv:1212.5701.
Zhang, F., Yuan, N.J., Lian, D., Xie, X., & Ma, W-Y. (2016). Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (pp. 353–362).
Zhang, H., Shen, F., Liu, W., He, X., Luan, H., & Chua, T-S. (2016). Discrete collaborative filtering. In Proceedings of the 21th acm sigkddinternational conference on knowledge discovery and data mining. (pp. 325–334).
Zhou, X., He, J., Huang, G., & Zhang, Y. (2015). SVD-based incremental approaches for recommender systems. Journal of Computer and System Sciences, 81(4), 717–733.
Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 61972439), Key Program in the Youth Elite Support Plan in Universities of Anhui Province (No. gxyqZD2020004 and No. gxyqZD2019010), Anhui Provincial Quality Engineering Teaching Research Project(No. 2018jyxm1100), and Natural Science Foundation of Anhui Province (No. 1908085MF190 and No. 1808085MF172).
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.
Rights and permissions
About this article
Cite this article
Sun, L., Liu, X., Liu, Y. et al. A novel deep recommend model based on rating matrix and item attributes. J Intell Inf Syst 57, 295–319 (2021). https://doi.org/10.1007/s10844-021-00644-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10844-021-00644-x