Research on e-commerce recommendation model based on CNN-LSTM
Personalized recommendation on the e-commerce platform is of great help in improving the user experience as well as the sales of goods,using the historical behavioral data of the users on the e-commerce platform can analyze the user's point of in-terest,and recommending and displaying for the user's interest can well improve the user experience and the marketing effect of the platform.Traditional recommender systems often use content similarity recommendation and collaborative filtering algorithms for recommendation,and these methods do not work well with large data volumes and sparse historical user behavior data.In order to solve these problems,a CNN-LSTM deep learning model based on the attention mechanism is proposed,and the effectiveness of the model is verified through comparative experiments.The experimental results show that the proposed model can solve some prob-lems in the traditional recommender system,and it is significantly helpful to improve the recommendation effect.
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