{"title":"基于遗传算法的产品推荐","authors":"U. Janjarassuk, S. Puengrusme","doi":"10.1109/ICEAST.2019.8802561","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided.","PeriodicalId":188498,"journal":{"name":"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Product recommendation based on genetic algorithm\",\"authors\":\"U. Janjarassuk, S. Puengrusme\",\"doi\":\"10.1109/ICEAST.2019.8802561\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided.\",\"PeriodicalId\":188498,\"journal\":{\"name\":\"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEAST.2019.8802561\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAST.2019.8802561","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided.
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