Research On Collaborative Filtering Recommendation Method Based On Knowledge Representation

At present,with the rapid development of information technology and more and more data related to people,it is difficult for users to find the content they are interested in in in the face of complex information,Recommendation system(recommendation system)came into being.Collaborative filtering recommendation algorithm is easy to understand and apply,and has been widely used in content recommendation.However,the traditional collaborative filtering recommendation algorithm relies too much on the user’s historical data,uses the historical data to generate a matrix,and generates the corresponding relationship through matrix decomposition.The amount of data is complex and incomplete,resulting in the decomposition of moments The array is too sparse,so it is difficult to find the closest user set and calculate the similarity.At the same time,the traditional collaborative filtering recommendation algorithm does not consider the semantic relationship between data,resulting in the lack of semantics of the recommendation results.Aiming at the problems of sparse data,lack of semantics and low recommendation efficiency in traditional collaborative filtering,this paper uses the clustering method to improve the recommendation efficiency based on the traditional collaborative filtering algorithm,and integrates the knowledge representation vector method to calculate the entity similarity and improve the recommendation semantics.This paper mainly solves the shortcomings of the traditional collaborative filtering algorithm from the following two aspects.In view of the large amount of user item matrix data in the traditional collaborative filtering recommendation algorithm and the high time complexity of recommendation,the clustering method is used to optimize,the user item matrix is obtained by using the user’s historical data,the data is processed by canopy and K-means,the number of cluster centers is obtained by canopy,and then the users are divided into several cluster clusters,It reduces the amount of calculation of each item similarity in the later stage and improves the recommendation efficiency.For the traditional recommendation algorithm does not consider the semantic relationship between users when recommending,a collaborative filtering recommendation method based on knowledge representation is proposed.The similarity is calculated by using the knowledge representation vector of entities.Firstly,the knowledge map is established,the similarity between entities is calculated by using the knowledge representation vector,and the sparse matrix is filled according to the similarity,At the same time,the inter entity information hidden in the original data is used to improve the semantics of the recommendation results and improve the existing problem of data sparsity.Finally,experiments are carried out on movielens data set.The results show that the proposed algorithm reduces the time complexity,improves the accuracy,reduces the mean value and effectively improves the recommendation efficiency compared with the traditional recommendation algorithm.