A Transformer-based Method for Recommending the Continuing Care Retirement Communities

A continuing care retirement community (CCRC) establishs large-scale, full-function and high-quality chain communities of medical and senior caring which combined physical and psycological feathures of the older adults by meeting multiple types of care to echo different levels of care needs for the older adults. In this paper, we propose a Transformer-based method for recommending the CCRCs. Our proposed Transformer-based method involves a 3-stages embedding layer and a self-attention layer. The first embedding stage is based on the care preference similarities of the older adults in the CCRCs. The second embedding stage is based on the promotion interaction preference similarities of the older adults out of the CCRCs. The thrid embedding stage is based on the social relationship similarities of the older adults. In self-attention layer, we multiply the length of query vector and that of key vector with the scaling factor of original Transformer to make the training process more stable.