文献类型：期刊文献

中文题名：Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

作者：Xiaoyu Sun[1,2];Zhou Huang[1,2];Xia Peng[3];Yiran Chen[1,2];Yu Liu[1,2]

第一作者：Xiaoyu Sun

机构：[1]Institute of Remote Sensing and Geographical Information Systems,Peking University,Beijing,People’s Republic of China;[2]Beijing Key Lab of Spatial Information Integration&Its Applications,Peking University,Beijing,People’s Republic of China;[3]Collaborative Innovation Centre of eTourism,Tourism College,Beijing Union University,Beijing,People’s Republic of China

第一机构：Institute of Remote Sensing and Geographical Information Systems,Peking University,Beijing,People’s Republic of China

年份：2019

卷号：12

期号：6

起止页码：661-678

中文期刊名：国际数字地球学报（英文）

外文期刊名：International Journal of Digital Earth

收录：Scopus;PubMed

基金：supported by grants from the National Key Research and Development Program of China[grant number 2017YFB0503602];the National Natural Science Foundation of China[grant number 41771425],[grant number 41625003],[grant number 41501162];the Beijing Philosophy and Social Science Foundation[grant number 17JDGLB002].

语种：英文

中文关键词：Recommendation system;geotagged photos;social media;model-based approach;support vector machine(SVM);gradient boosting regression tree(GBRT)

摘要：When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.