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Because renting a house is a low-frequency behavior of people, the housing rental suggestion issue suffers from extremely sparse data. In this paper, we propose to investigate the issue based on e-commerce data, as the two scenarios share the same users, and, more importantly, we can characterize the users in terms of their consumption attitudes and behaviors (that can direct people to rent houses) from the massive online shopping orders, and extract labeled users (users who have both housing and working addresses) from the delivery records. Though e-commerce data can be utilized, developing such an approach is still challenging. This happens because only a tiny part of these users can be used as supervised information and it is non-trivial to fuse the e-commerce data with geographic and traffic data. To this end, we first propose to formulate the task pairwise way to use the labeled users more effectively …