Open AccessInterpretable Recommendation System Based on Knowledge Map Features
Author(s) -
Hongyu Chen,
Jingyang Liu,
Mao Yang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/782/4/042013
Subject(s) - interpretability , computer science , scalability , object (grammar) , knowledge based systems , artificial intelligence , data science , knowledge base , recommender system , knowledge extraction , information retrieval , data mining , database
At present, it can be explained that the recommendation system is mostly designed for a specific recommendation model, and its scalability is weak. It is not enough for the emerging recommendation models, such as complex and mixed models with deep neural networks.Knowledge maps as a highly readable external knowledge carrier provide great possibilities for improving the ability of algorithms to interpret.In essence, the knowledge map is intended to describe the various entities or concepts and their relationships that exist in the real world, which constitute a huge semantic network diagram, nodes represent entities or concepts, and edges are composed of attributes or relationships.Based on the current research status, this paper analyzes and studies the concept of knowledge map, the combination of knowledge map and recommendation system, the object of interpretability and the application of knowledge map in interpretability model, combined with relevant recommendation models. The knowledge map, open up the relationship between the media, flexibly choose the most suitable medium according to the specific situation to recommend and explain the user.