KSR: Knowledge-based Sequential News Recommendation System

Abstract With the popularity of the Internet, some online news reading habits have gradually replaced traditional media devices. People can see a variety of news on mobile phones or web pages. The overloaded information makes it impossible for users to quickly get the news they want to see, so the existence of a news recommendation system is necessary. However, unlike other fields, the recommendation of news is time-sensitive, and the problem of sparse data makes traditional collaborative filtering algorithms invalid. There are many entities in the news, and the knowledge graph is a collection of a large number of entities. In this paper, we propose a KSR model (Knowledge-based Sequential Recommendation) which uses the knowledge graph as side information to enrich the feature representation of the news to calculate the user’s probability to click the forecasted news. KSR uses the knowledge graph to represent the entities in the news and uses a recurrent neural network to capture the sequential relationships in the news data to enrich the feature representation of the news. Because the different news that user has clicked have different relations with the news to be predicted, the attention model is introduced to calculate the weights. Finally, we conduct experiments on the existing dataset and results prove the efficacy of KSR over several baselines. Different contrast experiments also prove the effectiveness of each module of the model.