{"title":"下一个POI推荐系统:多视图表示学习在各种环境下的卓越表现","authors":"Yeonghwan Jeon, Junhyung Kim","doi":"10.1109/ICDMW58026.2022.00150","DOIUrl":null,"url":null,"abstract":"Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context\",\"authors\":\"Yeonghwan Jeon, Junhyung Kim\",\"doi\":\"10.1109/ICDMW58026.2022.00150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.\",\"PeriodicalId\":146687,\"journal\":{\"name\":\"2022 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW58026.2022.00150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context
Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.