Abstract
Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive.
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Acknowledgements
This research was supported by “the Fundamental Research Funds for the Central Universities” No. 3082016NS2016090.
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Yuan, W., Li, C., Guan, D. et al. Socialized healthcare service recommendation using deep learning. Neural Comput & Applic 30, 2071–2082 (2018). https://doi.org/10.1007/s00521-018-3394-4
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DOI: https://doi.org/10.1007/s00521-018-3394-4