id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-103242-78asegs6	Yang, Wenmian	Herding Effect based Attention for Personalized Time-Sync Video Recommendation	2019-05-02		.txt	text/plain	4321	278	62	However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Based on the above motivations and challenges, we propose an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA). Herding Effect Attention on above, we design an HEA mechanism, which calculates the influence weights of TSC contexts by their semantic similarities and timestamp intervals in an LSTM-based encoder-decoder framework.	./cache/cord-103242-78asegs6.txt	./txt/cord-103242-78asegs6.txt