Explicable recommendation based on knowledge graph

Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding-based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty.