{"title":"通过整合语义和时间因素以及聚类分析方法,改进协同过滤方法。","authors":"Ivohin Ye, Shelyakin G, Makhno M","doi":"10.15407/jai2024.01.057","DOIUrl":null,"url":null,"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","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving the methord of collaborative filtering by integrating semantic and temporal factors and the methord of cluster analysis.\",\"authors\":\"Ivohin Ye, Shelyakin G, Makhno M\",\"doi\":\"10.15407/jai2024.01.057\",\"DOIUrl\":null,\"url\":null,\"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. 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Improving the methord of collaborative filtering by integrating semantic and temporal factors and the methord of cluster analysis.
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
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.