Research Article
Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies
@INPROCEEDINGS{10.4108/eai.15-3-2024.2346153, author={Hongxing Tang and Jieying Zhong and Guanlin Liu}, title={Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies}, proceedings={Proceedings of the 5th International Conference on E-Commerce and Internet Technology, ECIT 2024, March 15--17, 2024, Changsha, China}, publisher={EAI}, proceedings_a={ECIT}, year={2024}, month={5}, keywords={deep learning; e-commerce; recommender systems; enterprise marketing; strategy}, doi={10.4108/eai.15-3-2024.2346153} }- Hongxing Tang
Jieying Zhong
Guanlin Liu
Year: 2024
Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies
ECIT
EAI
DOI: 10.4108/eai.15-3-2024.2346153
Abstract
Recommender systems could mitigate the problem of "information overload", understand the additional value of data, provide the specific information to costumer, and make information fully used. The integration of the characterization capability of deep learning (DL) with the recommendation system assists to deeply explore custumer requirements and offer details and specific recommendation services. The formulation of enterprise marketing strategy is a systematic problem that integrates various factors. DL technology can help enterprises take in opinions from their customers, optimize their marketing strategies, and improve their marketing results. This paper first introduce the merits and dismerits of conventional recommendation systems, and then review the latest achievement of DL system. At the same time, DL technology is applied to the formulation of enterprise marketing strategy. Finally, it analyzes and mention the future trend direction of intelligent recommendation systems
