An Explainable Knowledge Graph-Based News Recommendation System

The paper outlines an explainable knowledge graph-based recommendation system that aims to provide personalized news recommendations and tries to explain why an item is recommended to a particular user. The system leverages a knowledge graph (KG) that models the relationships between items and users’ preferences, as well as external knowledge sources such as item features and user profiles. The main objectives of this study are to train a recommendation model that can predict whether a user will click on a news article or not, and then obtain the explainable recommendations for the same purpose. This is achieved with three steps: Firstly, KG of the MIND dataset are generated based on the history and, the clicked information of the users, the category and subcategory of the news. Then, the path reasoning approaches are utilized to reach explainable paths of recommended news/items. Thirdly, the proposed KG-based model is evaluated using MIND News data sets. Experiments have been conduc ted using the MIND-demo and MIND-small datasets, which are the open-source English news datasets for public research scope. Experimental results indicate that the proposed approach performs better in terms of recommendation explainability, making it a promising basis for developing transparent and interpretable recommendation systems.