Collaborative filtering algorithm via compressing the sparse user-rating-data matrix
Zhang Wen-ge · Journal of Xidian University · 2009
The paper proposes a novel memory-based collaborative filtering algorithm—Multi-label Probabilistic Latent Semantic Analysis based Collaborative Filtering,which improves the quality of recommendations by reducing the dimension of the user-rating-data matrix by multi-label probabilistic latent semantic analysis when the matrix is extremely sparse.Firstly,it confines the set of latent variables of probability latent semantic analysis to the set of multi-label of items to make latent variables have meanings of corresponding labels.Then it learns the probabilistic distribution of latent variables,i.e.,the model of use's interest,to compress the user-rating-data matrix.Finally,it computes the similarity between different users based on the above learned model and makes recommendations.Compared to memory-based collaborative filtering algorithms,the proposed algorithm decreases the mean absolute error 4 percents averagely on test dataset by reducing the dimension of the user-rating-data matrix.The proposed algorithm makes the recommendation system understandable and obtains competitive recommendations compared to the filtering algorithm which reduces the dimension of the user-rating-data matrix by probabilistic latent semantic analysis.