{"title":"基于NLP技术的基于项目的协同过滤","authors":"V. Kovenko, I. Bogach, M. V. Baraban","doi":"10.31649/1999-9941-2021-51-2-17-22","DOIUrl":null,"url":null,"abstract":"The benchmark approach to content-based recommendation systems is exposed in this article. The usage of Word2Vec embeddings made by Google is unleashed. The opportunity of using additional business logic is considered.","PeriodicalId":121207,"journal":{"name":"Information Technology and Computer Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ITEM-BASED COLLABORATIVE FILTERING BASED ON NLP TECHNIQUES\",\"authors\":\"V. Kovenko, I. Bogach, M. V. Baraban\",\"doi\":\"10.31649/1999-9941-2021-51-2-17-22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The benchmark approach to content-based recommendation systems is exposed in this article. The usage of Word2Vec embeddings made by Google is unleashed. The opportunity of using additional business logic is considered.\",\"PeriodicalId\":121207,\"journal\":{\"name\":\"Information Technology and Computer Engineering\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Technology and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31649/1999-9941-2021-51-2-17-22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Technology and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31649/1999-9941-2021-51-2-17-22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ITEM-BASED COLLABORATIVE FILTERING BASED ON NLP TECHNIQUES
The benchmark approach to content-based recommendation systems is exposed in this article. The usage of Word2Vec embeddings made by Google is unleashed. The opportunity of using additional business logic is considered.