Collaborative filtering recommendation algorithm based on semantic similarity
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Abstract
To solve the problem that collaborative filtering recommendation algorithm does not consider the semantic relationship between recommendation objects,an improved collaborative filtering recommendation algorithm based on semantic similarity of recommendation objects is proposed. First,the semantic information of the recommended object is embedded into a low dimensional semantic space by using the knowledge map representation learning algorithm;then the semantic similarity between the recommended objects is calculated and integrated into the similarity calculation of collaborative filtering recommendation algorithm, thus compensating for the shortcoming that the collaborative filtering recommendation algorithm does not consider the semantic knowledge of the recommendation object. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional collaborative filtering recommendation algorithm.
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