Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation

ABSTRACT

The current e-commerce suggestion suffers from information overload, which makes it harder for customers to choose products. Due to this, researchers created a hybrid collaborative filtering algorithm and support vector machine e-commerce recommendation model to improve user recommendations. The support vector machine for effective offline classification also explains how to pick the right label threshold, the sparsity of the dataset, and other challenges from both the user- and item-oriented perspectives. According to the experimental results, the support vector machine classification methods based on user perspective and items from the Movielens dataset had average accuracies of 0.91 and 0.88, respectively, while the corresponding average accuracies on the Jester-joke dataset were 0.74 and 0.75. Both the user- and item-based collaborative filtering algorithms on the popular goods dataset generated noticeably fewer mean absolute errors than the cold goods dataset. The average coverage and accuracy of recommendations made using an e-commerce recommendation model that incorporates support vector machines and collaborative filtering algorithms are 90.2%, 92.8%, 88.4%, 83.7%, and 80.2%, respectively. This showed that the research methodology produces positive results in e-commerce recommendation.

KEYWORDS:

• CF
• SVM
• E-commerce
• recommendation models
• users
• projects

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Notes on contributors

Lan Chen

Lan Chen obtained her BE in E-commerce from Xichang College in 2010. She obtained her ME in Management Science and Engineering from Southwest Jiaotong University in 2013. Presently, she is working as a lecturer in the School of Business Administration, Sichuan Vocational College of Finance and Economics. Her areas of interest are e-commerce data analysis and online store operations.

Rui Xiong

Rui Xiong graduated from Xi’an Engineering University with a major in Art and Design in 2013. I obtained a Master’s degree in Art Theory from Sichuan Conservatory of Music in 2022. I am currently an assistant researcher at the School of Business Administration of Sichuan Vocational College of Finance and Economics. She excels in the fields of art design and e-commerce visual design.

Yifan Ji

Yifan Ji graduated from Communication University of China Nanjing with a major in Visual Communication in 2016. She obtained a Master’s degree in Art and Design from Jiangsu Normal University in2019. Presently, she is working as a lecturer in the School of Business Administration, Sichuan Vocational College of Finance and Economics. She is interested in e-commerce visual design and live streaming e-commerce.