Improving the methord of collaborative filtering by integrating semantic and temporal factors and the methord of cluster analysis.

Abstract

The article examines the algorithm for generating recommendations based on collaborative filtering, taking into account the influence of semantic and time factors and its improvement using cluster analysis methods in order to reduce the load on the recommendation system and improve the quality of recommendations by filtering out meaningless content and preserving the context during the generation of recommendations. The impact of semantic and time factors on the quality of the recommendation system (error in estimation approximation) and the application of the cluster analysis method on the speed of the system with a large set of data are analyzed. A technique for accelerating the processing of received data about users is proposed, which consists in an attempt to take into account the fact that users' interests change over time and the possibility of breaking down the content of statistical data by a set of specific features. A data preprocessing procedure (data aggregation) was formulated for the method of collaborative filtering based on comparisons of objects using the clustering method, which made it possible to reduce the complexity of calculations and, accordingly, the time for the formation of recommendations. An algorithm for calculating the object's assessment is presented, taking into account temporal and semantic factors. The software was developed, the adequacy of the proposed method was verified using data sets from different domain areas. As a result of the verification, it was found that the modified algorithm has better performance indicators compared to the naive method