Integrating Dynamic K-nearest Neighbor Slope_One into Collaborative Filtering Algorithm
Data sparse is a problem of traditional collaborative filtering algorithm,which will cause the algorithm to be insuffi-cient.The Slope_One algorithm is simple and efficient,and can predict the user's rating of an item.Therefore,this paper proposes a collaborative filtering recommendation algorithm combining dynamic K-nearest neighbor Slope_One to improve the accuracy of the algorithm.First,the improved cosine similarity formula is used to calculate the user similarity,K neighbor users are screened to cal-culate the average score deviation,the Slope_One algorithm is used to predict the corresponding user score,and effectively the score is filled into data matrix,and then the item-based collaborative filtering algorithm is used for recommendation.
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