Users' Preference Prediction of Real Estates Featuring Floor Plan Analysis using FloorNet
Users' Preference Prediction of Real Estates Featuring Floor Plan Analysis using FloorNet
复制标题
使用 FloorNet 进行平面图分析的用户对房地产的偏好预测
DOI：
10.1145/3210499.3210525
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Takemi Ohama
中科院分区：
文献类型：
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作者：
Naoki Kato;T. Yamasaki;K. Aizawa;Takemi Ohama
近年来，随着电子商务的发展，不仅推荐大量生产的日常物品，如书籍，而且推荐非大量生产的特殊物品已经成为一项重要任务。在这项研究中，我们提出了一个算法的真实的房地产推荐。世界上没有完全相同的房产，已经被别人占用的房产不能被推荐，用户一生中只租或买过几次房产。因此，自动属性推荐是最困难的任务之一。在这项研究中，我们预测用户的属性偏好，这是属性推荐的第一步，通过结合基于内容的过滤和多层感知器（MLP）。在MLP中，我们不仅使用用户和属性数据，而且还从物业平面图图像中提取的深层特征。结果，我们成功地预测了用户的偏好，准确率为60.7%。
In recent years, with the progress of e-commerce, recommendation for not only mass-produced daily items, such as books, but also special items that are not mass-produced has become an important task. In this study, we present an algorithm for real estate recommendation. There are no identical properties in the world, properties already occupied by someone else cannot be recommended, and users rent or buy properties only a few times in their lives. Therefore, automatic property recommendation is one of the most difficult tasks. In this study, we predict users' preference for properties, which is the first step of property recommendation, by combining content-based filtering and multilayer perceptron (MLP). In the MLP, we used not only attribute data of users and properties but also the deep features extracted from floor plan images of properties. As a result, we succeeded in predicting users' preference with an accuracy of 60.7%.