Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

中文翻译：

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基于卷积神经网络和基于矩阵分解的出行位置推荐方法

使用社区提供的经过地理标记的照片的旅行位置推荐方法是基于过去的签到信息。因此，这些方法不能有效地用于新的行驶位置，即它们遭受行驶位置冷启动问题。在这项研究中，我们提出了一种卷积神经网络和基于矩阵分解的出行位置推荐方法来解决该问题。具体而言，使用加权矩阵分解方法来获得行驶位置的潜在因子表示。通过使用卷积神经网络，可以根据其照片估算​​出新旅行地点的潜在因素表示。Flickr数据集上的实验结果表明，与现有方法相比，该方法可以提供更好的建议。