Marketing Recommendation Algorithm for New Media Platform Based on Data Analysis

Due to the problems of inaccurate content recommendation and low conversion rate on the current new media platform, this paper introduces an algorithm based on user behavior and content characteristics based on data analysis. Firstly, this paper collects user behavior data and content feature data through cooperation with new media platforms. The original data is duplicated, missing values are processed, and abnormal values are corrected. At the same time, the data is formatted and standardized. The paper also extracts keywords, themes, emotions and other features from the text content through natural language processing technology. The random forest method helps to further screen out the feature subsets that have an impact on the recommendation effect. On the basis of feature extraction and selection, a comprehensive recommendation algorithm model is constructed. Collaborative filtering algorithm can mine the similarity between users based on their historical behavior data. Content recommendation algorithm focuses on the analysis and matching of content characteristics, and recommends content that meets users' interests. The hybrid recommendation model is superior to the single collaborative filtering algorithm and content recommendation algorithm in terms of click-through rate, user retention time and conversion rate. The maximum residence time of users in the mixed model is 18.9 seconds, while the maximum residence time in the single algorithm is lower. The hybrid recommendation model shows a good application prospect in the marketing recommendation of new media platforms, which can optimize the marketing effect.