Algorithms for the Formation of Recommendations in the Information System
Keywords:algorithm, filtration, matrix dimensios, recommendation system, sparsity
The article deals with the problem of scalability and dimension reduction of data in the algorithms of recommendations. It is proposed to improve the item-to-item algorithm by excluding from the user-item matrix elements that that do not have enough estimates. Thus more denser data are used that allows to receive more exact results. Also due to the fact that the dimension of the user-item matrix decreases, the execution time of the algorithm decreases. To solve the problem, the Tachimoto coefficient, the cosine measure, the Pearson correlation coefficient and the Euclidean distance are used to calculate the degree of similarity of the elements. The efficiency of the usual item-to-item algorithm and the algorithm were compared using only the active values in the user-item matrix. The obtained results confirm the efficiency of the item-to-item algorithm based on a dense matrix. The obtained results can be used to optimize the operation of any recommendation system.
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