Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are not really interested in. To address the issues of mining users' actual preferences from users' recent check-ins, this paper proposes a Convolutional Neural Networks with Attention (CNNA), which consists of a latent representation method and a deep convolutional neural network. We have carried out

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... sets of experiments to test the validity of CNNA, and the experimental results show the effectiveness of CNNA. Abstract Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are not really interested in. To address the issues of mining users' actual preferences from users' recent check-ins, this paper proposes a Convolutional Neural Networks with Attention (CNNA), which consists of a latent representation method and a deep convolutional neural network. We have carried out several sets of experiments to test the validity of CNNA, and the experimental results show the effectiveness of CNNA.