id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-024430-r0gbw5j6	Wang, Hao	Modeling Users’ Multifaceted Interest Correlation for Social Recommendation	2020-04-17		.txt	text/plain	3623	218	51	Many methods have been proposed for social recommendation in recent years, and these methods can be mainly grouped into two categories: (1) memory-based methods [1, 12, 14] use social relation as an indicator that filters relevant users and directly recommend friends' visited items to a user; (2) model-based methods [4, 5, 9, 10, 22, 27, 29, 31] integrate social relation into factorization methods to constrain that friends share similar interest embeddings. We propose to use a correlation vector, instead of a scalar value, to characterize the interest correlation between each pair of friends, and design a dimension-wise attention mechanism with the social network as input to learn it. To accommodate our problem, we further design a dimension-wise attention mechanism and use it to learn a correlation vector for each pair of friends, building their multi-dimensional interest correlation for social recommendation.	./cache/cord-024430-r0gbw5j6.txt	./txt/cord-024430-r0gbw5j6.txt