Collaborative Filtering Algorithm Based on Reducing Data Sparsity
Collaborative filtering algorithm is a common algorithm in recommendation systems,and its core idea is to mine user preferences through historical data and calculate similar neighbor items of objects for recommendation.However,the general real data has a serious data sparsity,and there are too few common scoring items between users or projects,which makes some traditional similarity al-gorithms inaccurate in calculation and low in recommendation accuracy.The traditional Slope One algorithm is inaccurate,but it has simple implementation and high operation efficiency,which can be used as sparse data pre-filling to improve the accuracy of similarity calculation.Therefore,we introduce a collaborative filtering algorithm based on reducing data sparsity,incorporating the Slope One algorithm.Firstly,hierarchical clustering is performed on the user rating data,and then the Weighted Slope One algorithm is used to predict and fill in some blank data of the high-sparsity dataset,thereby significantly reducing the data sparsity and improving the accuracy of Pearson's similarity calculation.Finally,the object attribute preference similarity is introduced for fusion.Validation is performed using the MovieLens 100 K dataset,and the results clearly show a reduction in the Mean Absolute Error(MAE),indicating an improvement in recommendation accuracy.It is validated that the proposed algorithm can enhance recommendation accuracy to some extent.
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