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Relation Embedding for Personalised Translation-Based POI Recommendation
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206191/ http://dx.doi.org/10.1007/978-3-030-47426-3_5 |
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author | Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr |
author_facet | Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr |
author_sort | Wang, Xianjing |
collection | PubMed |
description | Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model. |
format | Online Article Text |
id | pubmed-7206191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72061912020-05-08 Relation Embedding for Personalised Translation-Based POI Recommendation Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr Advances in Knowledge Discovery and Data Mining Article Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model. 2020-04-17 /pmc/articles/PMC7206191/ http://dx.doi.org/10.1007/978-3-030-47426-3_5 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr Relation Embedding for Personalised Translation-Based POI Recommendation |
title | Relation Embedding for Personalised Translation-Based POI Recommendation |
title_full | Relation Embedding for Personalised Translation-Based POI Recommendation |
title_fullStr | Relation Embedding for Personalised Translation-Based POI Recommendation |
title_full_unstemmed | Relation Embedding for Personalised Translation-Based POI Recommendation |
title_short | Relation Embedding for Personalised Translation-Based POI Recommendation |
title_sort | relation embedding for personalised translation-based poi recommendation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206191/ http://dx.doi.org/10.1007/978-3-030-47426-3_5 |
work_keys_str_mv | AT wangxianjing relationembeddingforpersonalisedtranslationbasedpoirecommendation AT salimflorad relationembeddingforpersonalisedtranslationbasedpoirecommendation AT renyongli relationembeddingforpersonalisedtranslationbasedpoirecommendation AT koniuszpiotr relationembeddingforpersonalisedtranslationbasedpoirecommendation |