id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020901-aew8xr6n	García-Durán, Alberto	TransRev: Modeling Reviews as Translations from Users to Items	2020-03-17		.txt	text/plain	5037	317	57	TransRev learns vector representations for At training time, a function's parameters are learned to compute the review embedding from the word token embeddings such that the embedding of the user translated by the review embedding is similar to the product embedding. Methods that fall into this category such as [31, 32] learn latent representations of users and items from the text content so as to perform well at rating prediction. Similar to sentiment analysis methods, TransRev trains a regression model that predicts the review rating from the review text. We compare to the following methods: a SVD matrix factorization; HFT, which has not often been benchmarked in previous works; and DeepCoNN [38] , which learns user and item representations from reviews via convolutional neural networks. Representation learning of users and items for review rating prediction using attention-based convolutional neural network	./cache/cord-020901-aew8xr6n.txt	./txt/cord-020901-aew8xr6n.txt