Academic Journal of Computing & Information Science, 2023, 6(2); doi: 10.25236/AJCIS.2023.060206.
Guangzhou Institute of Science and Technology, Guangdong, Guangzhou, China, 510540
Nowadays, information plus Internet technology is continuously developing, and people have entered the era of information overload from the era of lack of information access. In this context, it is difficult to find one's own "tailor-made" information from the huge amount of information. In this context, the recommendation system came into being. This article uses the order data under an e-commerce platform, uses user-based and item-based collaborative filtering algorithms to personalize products, and builds a product recommendation system. Through the feasibility of actual application scenarios and the prediction score of the model, a series of evaluations were made on the algorithm, and reliable and effective prediction results were obtained from the three indicators of accuracy, recall and F1 score. The final recommendation effect of the TopN recommendation list obtained was significant, and the recommendation efficiency could reach 85%.Provide some ideas for the research and development direction of modern recommendation algorithms.
Recommended system; Collaborative filtering; Personalize; Algorithm model
Zhong Zichao. Product Personalized Recommendation Engine Based on Collaborative Filtering Algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 2: 36-43. https://doi.org/10.25236/AJCIS.2023.060206.
 Sun Jixiang. Research and Application of User Portrait in Recommendation System [D]. Beijing: North University of Technology, Master's Thesis, 2020.
 Zhao Liang, Hu Naijing, Zhang Shouzhi,et al. Design of Personalized Recommendation Algorithm [J]. Computer Research and Development, 2002, 39(8):986-991.
 Chang Hao, Yang Shengquan, et al. Research on Product Recommendation Algorithm Based on Collaborative Filtering Decision tree [J]. Value Engineering, 2020,9(52):127-128.
 Fan Zezhou. Research on the Online Shopping Guide Product Recommendation System of Company A [D]. Beijing: Beijing Jiaotong University, Master's Thesis,2019.
 Gao Jian. Research and Implementation of Personalized Recommendation Algorithm on WeChat E-commerce Platform [D]. Lanzhou: Chang'an University, Master's Thesis,2019.
 Gao Yukai, Wang Xinhua, Guo Lei, et al. User Cold Start Recommendation Algorithm Based on Collaborative Matrix Decomposition[J]. Computer Research and Development , 2017,8: 188-198.
 LindenG, SmithBR, YorkJC, et al. Amazon. Comrecommendations: Item-to-item Collcaborative Filtering[J]. IEEE Internet Computing, 2003,7(1):76-80.