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With the continuous progress of world economy and human society, people&#x2019;s life span is increasingly growing and as a result, the whole world is stepping into the aging society. In the aging society, the elder people generally call for specialized healthcare services that are often provisioned by their residential areas or houses or communities. In this situation, recommending appropriate houses or real estates to the elder people becomes a necessity to help create a healthy elderly care service community, which contribute much to the stability and health of the whole nation even all the world. However, the personalized preferences of the elder people are often hard to capture and profile as the requirements from the elder people are generally vague and undetermined. Moreover, the profiles of the elder people stored in different cloud platforms are often sensitive enough, which block the rational and full use of the valuable elder people profiles for better elder people preference prediction. Considering the above two challenging issues, in this research work, we propose a privacy-aware real estate recommendation method for elderly care based on the historical consumption behaviors of the elder people stored in cloud platforms. At last, the effectiveness and efficiency of the proposal are validated by a series of experiments based on a real-world dataset. Comparison results show the feasibility of the proposal in this research work.

Privacy-Aware Real Estate Recommendation in Cloud for Elderly Care Based on Historical Consumption Behaviors