Application of Data Processing Technology to Collaborative Filtering Algorithm in E-Commerce Collaboration

With the rapid changes in technology and market environment, innovative models in the e-commerce industry emerge in an endless stream, and these models often have great uncertainty. How to systematically summarize and standardize different innovative models and form an effective research framework remains a challenge. To this end, this study introduces a collaborative filtering algorithm to mine potential cooperative relationships based on the user-item relationship matrix and recommend the optimal partner combination to build an efficient cooperative network. In order to improve the accuracy of the model, similarity calculation and score prediction methods are also used to achieve resource matching and cooperation path optimization. In addition, the optimization of the dynamic collaborative network combines real-time data updates and online learning algorithms to enhance the dynamic adaptive ability of the network. The combination of stream data processing technology and online collaborative filtering algorithms effectively improves the network’s response speed to market changes and reduces resource consumption. Through multi-objective optimization and feedback mechanism, the algorithm strikes a balance between collaborative efficiency, cost and benefit, and improves the modeling accuracy and robustness of heterogeneous data through multimodal data fusion. The research results show that the algorithm in this paper has made breakthroughs in both indicators, with an accuracy rate of 0.91 and a recall rate of 0.80. The application of collaborative filtering algorithm in collaborative innovation of e-commerce industry has significantly improved the resource integration efficiency and synergy effect of collaborative network.