Simulation of Collaborative Filtering Algorithm for Book Recommendation Considering Data Sparsity
At present,book recommendation algorithms tend to ignore the problem of data sparsity,leading to a large error between recommendation results and content.As a result,a collaborative filtering algorithm for book recom-mendation was proposed based on data sparsity.Firstly,the data was preprocessed.Based on the comprehensive trust analysis between users,the user interest was modeled by the distribution estimation algorithm.Secondly,a set of user interest clusters was constructed to divide user interests.Moreover,the neighbor closest to the retrieved object was se-lected.Furthermore,the scores of adjacent items were calculated and ranked in descending order.The top items were the book recommendation results.Experimental results show that the proposed algorithm takes less than 12s to recom-mend 500 books.Meanwhile,this algorithm reduces the average absolute error and root mean square error,thus achie-ving the most accurate book recommendation.
Data sparsityBook recommendationCollaborative filtering algorithmUser interest modelCompre-hensive trust