Research on Product Recommendation Algorithm Based on Artificial Intelligence

With the rapid development of e-commerce, product recommendation system has become a key factor to improve user experience and increase sales. This paper proposes a new Model, Hybrid Intelligent Recommendation Model (HIRM), which combines the advantages of collaborative filtering (CF), deep learning (DL) and matrix decomposition (MF). By analyzing user behavior data, commodity attributes and implicit feedback information, HIRM first uses CF to capture the potential relationship between users and commodities, then extracts implicit features through MF, and finally introduces DL framework to enhance learning ability and generate more accurate recommendation results. The model effectively solves the sparsity problem in the traditional algorithm, and significantly improves the accuracy and personalization level of recommendation. The experimental results show that HIRM outperforms single models and other combined models on multiple public data sets, providing a new perspective and method for the field of product recommendation.