HRET: Heterogeneous Information Network for Recommendation in testing and inspection

With the help of the sufficiency of heterogeneous information, heterogeneous information network(HIN) has been treated as the most advanced method to extract complex semantic data in recommender system. But it is still an empty field for some traditional industries such as testing and inspection industry, which mainly adopt the similarity-based collaborative filtering(CF) method. But it will make a huge waste of the rich heterogeneous auxiliary data, which could be fully utilized by HIN based method. Especially for testing and inspection industry, the profession will help the model to find a more accurate match between the user and business. In this work, we succeeded in building up a HIN embedding approach for recommendation, and design a unique network structure for testing and inspection industry, which both utilize the rich underlying information and properly solve the specialty problem in a professional industry, different from normal recommender scenario. An intensive experiment on the real world data set shows the performance of the model.