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, the target of which is to evaluate a user’s satisfaction degree for a given house. Thereafter, we carefully design features of users and houses from the multi-source data and devise a novel deep network, entitled HouseCritic, to fuse the features and tackle the formulated problem. Notably, HouseCritic introduces meta-learning technology to design a fusion structure that can explicitly and effectively fuse extracted features and accordingly infer a satisfaction degree. Moreover, HouseCritic proposes to leverage unsupervised information, capable of effectively fusing data when the supervised information is insufficient. Finally, we conduct experiments on real-world data collected from Beijing, China. Experimental results demonstrate the advantages of the proposed approach over several baselines. Moreover, a system with the approach is being deployed in an e-commerce company.