Wei Song
Cong Wang
ABSTRACT
Deep learning (DL) is playing an increasingly important role in the field of recommender systems (RSs). In this paper, we enhance the performance of a DL-based RS by incorporating matrix factorization (MF), which gained a great deal of popularity as a result of the Netflix Prize competition. Thus, DL is responsible for learning the nonlinear relationship between users and items, whereas MF is used to describe the linear relationship between users and items. We use the typical DL architecture of the multilayer perceptron, and use layer normalization and the residual to improve its performance. Our experimental results showed that the proposed method can make recommendations accurately.
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 770-778.Google Scholar
Cross Ref
- Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, and Enhong Chen. 2020. Sampling-decomposable generative adversarial recommender. In Proceedings of the Annual Conference on Neural Information Processing Systems.Google Scholar
- Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac. 2018. News recommender systems - survey and roads ahead. Inf. Process. Manag. 54, 6 (November 2018), 1203-1227.Google ScholarCross Ref
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (August 2009), 30-37.Google ScholarDigital Library
- Hongyang Liu, Zhu Sun, Xinghua Qu, and Fuyong Yuan. 2021. Top-aware recommender distillation with deep reinforcement learning. Inf. Sci. 576 (October 2021), 642-657.Google Scholar
- Liu Na, Ming-Xia Li, Qiu Hai-yang, and Hao-Long Su. 2021. A hybrid user-based collaborative filtering algorithm with topic model. Appl. Intell. 51, 11 (November 2021), 7946-7959.Google ScholarDigital Library
- Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In Proceedings of the Fourteenth ACM Conference on Recommender Systems. 240-248.Google ScholarDigital Library
- Qusai Shambour. 2021. A deep learning based algorithm for multi-criteria recommender systems. Knowl. Based Syst. 211 (January 2021).Google Scholar
- Wei Song and Xuesong Li. 2019. A non-negative matrix factorization for recommender systems based on dynamic bias. In Proceedings of the 16th International Conference on Modeling Decisions for Artificial Intelligence. 151-163.Google ScholarDigital Library
- Wei Song, Pengwei Shao, and Peng Liu. 2019. Hybrid recommendation algorithm based on weighted bipartite graph and logistic regression. In Proceedings of the Second CCF International Conference on Artificial Intelligence. 159-170.Google ScholarCross Ref
- Wei Song and Kai Yang. 2014. Personalized recommendation based on weighted sequence similarity. In Proceedings of the 8th International Conference on Intelligent Systems and Knowledge Engineering. 657-666.Google ScholarCross Ref
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1235-1244.Google ScholarDigital Library
- Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin. 2019. Understanding and improving layer normalization. In Proceedings of the Annual Conference on Neural Information Processing Systems. 4383-4393.Google Scholar
- Xiaofeng Yuan, Lixin Han, Subin Qian, Guoxia Xu, and Hong Yan. 2019. Singular value decomposition based recommendation using imputed data. Knowl. Based Syst. 163, 1 (January 2019), 485-494.Google ScholarCross Ref
- Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, and Guandong Xu. 2021. Where are we in embedding spaces? In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2223-2231.Google ScholarDigital Library
- Hybrid Recommendation Based on Matrix Factorization and Deep Learning
Recommendations
A preprocessing matrix factorization on collaborative filtering based library book recommendation system
DSIT '18: Proceedings of the 2018 International Conference on Data Science and Information TechnologyNowadays, recommendation systems are widely used to recommend items to the users that are specific to their individual preferences and most appropriate. For this reason, many academic libraries try to establish an effectiveness and efficiency book ...
Recommending items to group of users using Matrix Factorization based Collaborative Filtering
Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users. Several group recommender systems have been proposed by extending traditional KNN based Collaborative ...
Attributes coupling based matrix factorization for item recommendation
Recommender systems have attracted lots of attention since they alleviate the information overload problem for users. Matrix factorization is one of the most widely employed collaborative filtering techniques in the research of recommender systems due ...
Comments