{"title":"使用谷歌位置推荐个性化的旅游景点","authors":"Maya Sappelli, S. Verberne, Wessel Kraaij","doi":"10.1145/2484028.2484155","DOIUrl":null,"url":null,"abstract":"The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline - Google Places only -- run.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Recommending personalized touristic sights using google places\",\"authors\":\"Maya Sappelli, S. Verberne, Wessel Kraaij\",\"doi\":\"10.1145/2484028.2484155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline - Google Places only -- run.\",\"PeriodicalId\":178818,\"journal\":{\"name\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2484028.2484155\",\"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 36th international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2484028.2484155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recommending personalized touristic sights using google places
The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline - Google Places only -- run.