With the continuous growth of China's e-commerce industry, the user base for online shopping is expanding, and e-commerce platforms are encountering bottlenecks in their development. The issue of information overload has made it increasingly challenging for consumers to locate the specific products they intend to purchase among the vast array of available options. Given the extensive range of products on e-commerce platforms, traditional collaborative filtering methods struggle with sparse user-item matrices, and less popular items face challenges in receiving recommendations. This paper introduces several algorithms, including collaborative filtering and cosine similarity-based algorithms.To enhance the quality of the recommendation system, this paper incorporates users' feedback coefficients on products into the penalty factor. Additionally, by integrating user portraits obtained through cluster analysis, a collaborative filtering algorithm based on penalty factor correction is proposed.

Chen Shuangyu, Zhang Bin, Chen Junhao, Zhou Zihan, Shen Yibo, Xiang Weitao. Reproduction Study of Item-CF Algorithm Based on Penalty Factor Correction. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 199-206. https://doi.org/10.25236/AJCIS.2024.070526.

[1] Bian, L.(2020). Research on User Recommendation Algorithms Based on E-commerce Platforms. Nanjing University .

[2] Shi, X.(2020). Research on Personalized Recommendation Algorithms in E-commerce Considering Price Factors. Tianjin University.

[3] 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.

[4] Wu, Y., Shen, J., Gu, T. Z., Chen, X. H., Li, H., & Zhang, S. (2007). Algorithm for sparse problem in collaborative filtering. Jisuanji Yingyong Yanjiu/ Application Research of Computers, 24(6), 94-97.

[5] Kuo, R. J., Liao, J. L., & Tu, C. (2005). Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce. Decision Support Systems, 40(2), 355-374.

[6] Chen, P., Chen C., Hong Y.(2016).A Collaborative Filtering Recommendation Algorithm Combining Association Rules. Small Microcomput Syst 37(2) : 287-292.

[7] Deng, A., Zhu, Y., Shi, B.(2023).Collaborative Filtering Recommendation Algorithm Based on Predicting Item Ratings.Journal of Software, 14(9): 1621-1628.

[8] Zhang, Y., Dai, J., Xiong, Z., et al.(2013)Collaborative Filtering Algorithm with Stepwise Padding to Alleviate Data Sparsity.Computer Application Research, 30(9): 2602-2605.

[9] Deng, A., Zuo, Z., Zhu,Y.(2004). Collaborative Filtering Recommendation Algorithm Based on Item Clustering.Small Microcomput Syst, 25(9): 1665-1670.

[10] Liu, X., Ge, J., Chen,D.(2010). A Combined Recommendation Algorithm Based on Clustering and Collaborative Filtering.Computer Engineering and Science, 32(12): 125-127.

[11] Wang, Y., Liu K.(2020).An Optimization Clustering Collaborative Filtering Recommendation Algorithm.Computer Engineering and Applications, 56(15): 66-73.

[12] Xu, F.(2020). Implementation of a Collaborative Filtering Algorithm with Improved Similarity. Electronic Technology, 33(2): 54-59.

[13] Bao, K., Liu, Q.(2019).Research on Parallel Algorithm for Combining Naive Bayes and Collaborative Filtering in Takeout Recommendation.Computer Application and Software, 36(11): 250-255, 285.

[14] Gu, M., Huang, W., Huang, Y., et al.(2020)Combining User Clustering and Improved User Similarity in Collaborative Filtering Recommendation.Computer Engineering and Applications, 56(2): 185-190.