A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph

With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions. (C) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Automated summary

The paper analyzes the theme of recommendation system using literature data, identifies research directions and hotspots through statistics and analysis, and explores potential future research directions and solutions.