An Overview of Heterogeneous Information Networks based on Recommendation System

Abstract

Heterogeneous Information Networks (HINs) provide a natural way to represent different relationships among entities of different kinds, as a consequence they are valuable in many domains. Analysing and extracting understanding from HINs normally is based at the idea of meta paths, which are paths in the community schema denoting family members of different semantics among entities. Moreover, real-global HINs are frequently extremely big, containing thousands and thousands of nodes and edges. Therefore, exploring and analysing HINs not only requires interdisciplinary knowhow, having the ability both to interpret and pick out suitable meta paths inside the network, but additionally to run the analysis in an efficient and scalable manner. However, there may be a loss of tools to facilitate this assignment. Most real system include a huge wide variety of interacting, multityped components, while most current researches version them as homogeneous networks, without distinguishing different forms of objects and links in the networks. In recent years, an increasing amount of works have been proposed to present helper data in recommender system to alleviate information sparsity and cold-start issues. Among them, heterogeneous information networks (HIN)- based recommender systems give a brought together way to deal with meld different helper data, which can be joined with standard suggestion calculations to successfully upgrade the presentation and interpretability of models, and accordingly have been applied in many kinds of recommendation tasks. This paper gives a far reaching and efficient overview of HIN-based recommender systems, including four perspectives: ideas, strategies, applications, and resources.