[Objective] This paper tries to identify information needed by the users, and then makes timely and accurate recommendations. [Methods] First, we generated the candidate set through content-based recommendation algorithm and item-based collaborative filtering algorithm. Then, we used parallel MapReduce technique to improve the parallel data mining performance of the proposed method. Finally, we adopted machine learning algorithms to increase the accuracy of recommended candidates and referred, personalized documents to the users. [Results] We created the recommendation list based on articles checked by the individual user. The model’s evaluation accuracy was 78.5%, and its mean squared error was 0.22. [Limitations] The user and text features need to be further investigated. The accuracy of word segmentation and model training algorithm needs to be optimized. [Conclusions] The proposed model generates personalized recommendation lists for users, and provide good support for related services.
( Weng Xiaolan, Wang Zhijian. Research Process of Collaborative Filtering Recommendation Algorithm[J]. Computer Engineering and Applications, 2018,54(1):25-31.)
[2]
何安. 协同过滤技术在电子商务推荐系统中的应用研究[D]. 杭州:浙江大学, 2007.
[2]
( He An. Research on Collaborative Filtering Technologies of Recommendation System for E-Commerce[D]. Hangzhou: Zhejiang University, 2007.)
[3]
张颖. 基于混合机制的新闻推荐系统研究[D]. 哈尔滨:哈尔滨工业大学, 2015.
[3]
( Zhang Ying. Research on News Recommendation System Based on Hybrid Mechanism[D]. Harbin: Harbin Institute of Technology, 2015.)
[4]
Chen H, Li Z, Hu W. An Improved Collaborative Recommendation Algorithm Based on Optimized User Similarity[J]. The Journal of Supercomputing, 2016,72(7):2565-2578.
doi: 10.1007/s11227-015-1518-5
( Qian Chunlin, Zhang Xingfang, Sun Lihua. Advanced Collaborative Filtering Recommendation Model Based on Sentiment Analysis of Online Review[J]. Journal of Shandong University: Engineering Science, 2019,49(1):47-54.)
( Yang Jiali, Li Zhixu, Xu Jiajie, et al. An Adaptive Hybrid Collaborative Filtering Recommendation Algorithm[J]. Computer Engineering, 2019,45(7):222-228.)
doi: 10.19678/j.issn.1000-3428.0051041
[7]
Zhao W, Wang B, Yang M, et al. Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training[J]. IEEE Transactions on Cybernetics. DOI: 10.1109/TCYB.2019.2896766.
doi: 10.1109/TCYB.2020.2997943
pmid: 32584775
[8]
Sun F, Liu J, Wu J, et al. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer[OL]. arXiv Preprint, arXiv: 1904. 06690.
( Zhang Xingyu. Microblog Topic Hybrid Recommendation Algorithm Based on Collaborative Filtering and Content Filtering[[J]. Computer Programming Skills and Maintenance, 2019(3):52-54.)
[10]
Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms [C]//Proceedings of the 10th International Conference on World Wide Web. 2001.
( Fan Zhiqiang, Zhao Wentao. Modified Content-based Collaborative Film Recommendation Algorithms[[J]. Information and Computer: Theoretical Edition, 2019(13):42-43,47. )
( Gong Keyu, Zhang Yichi. TF-IDF-based Feature Extraction Method for Ancient Text Content[[J]. Electronic Technology & Software Engineering, 2019(17):130-131.)
( Liu Diyong, Yang Qiang. Research on Nuclear Power Document Personalized Recommendation System Based on Machine[J]. Power Systems and Big Data, 2019,22(9):43-48.)
( Wang Weihong, Zeng Yingjie. Collaborative Filtering Recommendation Algorithm Based on Clustering and User Preference[J]. Computer Engineering and Applications, 2020,56(3):68-73.)