Accurate recommendation method for enterprise product network marketing information under the background of big data

Aiming to achieve personalised and precise recommendation of marketing information, a method for accurate recommendation of enterprise product network marketing information under the background of big data is proposed. Firstly, collect user information data and preprocess the data to construct a user profile that comprehensively describes user interests and preferences based on the data processing results. Secondly, a collaborative filtering algorithm based on users and items is adopted for predicting user preferences. Finally, the three-dimensional features of marketing information are obtained through the serial parallel convolutional gate valve recurrent neural network in deep learning, and combined with user profiles and preference prediction results, the matching between users and marketing information is achieved, thereby realising personalised recommendation of marketing information. The experimental results show that the proposed method has high recommendation accuracy, high user satisfaction, and high data processing efficiency, indicating its good application effect.