News personalized recommendation based on event ontology
摘要
Aiming at the problems existing in the traditional recommendation system,such as cold start,sparse data,lack of semantics and relatively low recommendation accuracy,a recommendation algorithm based on event ontology is proposed.By combining the news classification structure and news corpus to build event ontology,the elements of news browsed by users are extracted and user’s interest model is constructed.The similarity between the news events is calculated based on the event ontology structure,the user interest similarity is calculated through user’s interest model,and relevant news events are found according to the semantic radius of the non-hierarchical structure of event ontology.Synthesize the news ontology similarity,the user’s interest similarity,and the non-hierarchical structure similarity to realize a comprehensive news personalized recommendation.Experimental results show that the proposed algorithm has better recommendation than the collaborative filtering recommendation algorithm and the recommendation algorithm based on content.