Research on Algorithm of Intelligent Recommendation System Based on Big Data

The research aims to solve the challenge of filtering out the content that users are really interested in from massive data. In this paper, an intelligent recommendation system named multi-source fusion deep collaborative recommendation algorithm (MSDC-Rec) is proposed, which combines collaborative filtering, content filtering and deep learning (DL) technology to improve the accuracy and diversity of the recommendation system. MSDC-Rec algorithm integrates user behavior data, item attribute data and social network information, and uses deep neural network for advanced feature extraction and pattern learning, thus accurately portraying user portraits and realizing personalized and accurate recommendation services. The experimental results show that MSDC-Rec is superior to the traditional algorithm in accuracy, recall, F1 score and personalized index, and has low root mean square error (RMSE) and acceptable real-time. This study not only solves the problems of data sparsity and cold start, but also significantly improves the accuracy and personalization of recommendation, which provides a new idea for the development of intelligent recommendation system.