In order to solve the problem of information overload in big data era, the personalized recommender system has been widely used. Collaborative filtering, as a classical algorithm, has become the basis of the recommender system. In recent years, there are more and more recommender systems based on multiple data sources are proposed. Today's recommender systems integrate multiple data sources and recommendation methods are more accurate and explainable compare with rating-based recommendation

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... ems. How to integrate multiple data sources to further improve the accuracy and interpretability of recommendation results, reduce computational complexity and cold start risk has become the key content of recommendation researches.