A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. (1st March 2021) - Shao, Bilin; Li, Xiaojun; Bian, Genqing
Highlights: Revealing research hotspot and corresponding characteristics by bibliometric methods. Discovering underlying laws behind data by constructing scientific knowledge graph. Summarizing potential research hotspots by keyword co-occurrence cluster graph. Summarizing cutting-edge trends by keyword co-occurrence cluster graph. Discussing the main open problems in depth and proposing corresponding solutions. Abstract: 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.