Research on AI-driven social media recommendation strategies for traditional cultural content
Hui Wang
Communication University of China, Nanjing
Haiying Liang
Communication University of China, Nanjing
DOI: https://doi.org/10.59429/esta.v13i1.13385
Keywords: artificial intelligence; social media; traditional culture; content recommendation; multimodal integration
Abstract
The algorithmic distribution of social media has changed the traditional pathways of cultural dissemination, with both trending and fragmented content coexisting, which can easily lead to symbolic consumption and shifts in value. This article takes a humanities-oriented approach, exploring methods such as semantic modeling, dynamic profiling, weight adjustment, collaborative production, and multimodal experiences to enhance the interpretability and transmission effectiveness of recommendations. It aims to provide strategic reference for platform governance and high-quality content supply, while promoting sustained cultural understanding among audiences during their routine browsing.
References
[1] Wang Yan. Research on Question Recommendation Strategies for Social Q&A Platforms Based on User Classification [J]. Knowledge Economy, 2025, (30): 113-116.
[2] Qu Dingqin. Optimization Strategies for Micro-Drama Content Production under Intelligent Recommendation Algorithms [J]. Audio-Visual Theory and Practice, 2024, (03): 71-76.