Deep learning techniques in e-commerce recommender  systems and their impact on business marketing  strategies

Tang, Hongxing; Zhong, Jieying; Liu, Guanlin

Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies

Tang, Hongxing and Zhong, Jieying and Liu, Guanlin (2024) Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies. In: Proceedings of the 5th International Conference on E-Commerce and Internet Technology, ECIT 2024, March 15–17, 2024, Changsha, China.

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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

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Deposited:	04 Mar 2026 17:36
Last Modified:	16 Apr 2026 23:36
URI:	http://eprints.eai.eu/id/eprint/49083

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