{"title":"推荐系统的实用表示学习","authors":"O. Zakharchuk","doi":"10.1145/3176349.3176900","DOIUrl":null,"url":null,"abstract":"The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Practical Representation Learning for Recommender Systems\",\"authors\":\"O. Zakharchuk\",\"doi\":\"10.1145/3176349.3176900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.\",\"PeriodicalId\":198379,\"journal\":{\"name\":\"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3176349.3176900\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3176349.3176900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Practical Representation Learning for Recommender Systems
The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.