Abstract
Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
Original language | English |
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Title of host publication | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery, Inc |
Pages | 1981–1984 |
ISBN (Print) | 9781450380164 |
DOIs | |
Publication status | Published - 25 Jul 2020 |
Event | 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval - Online Conference Duration: 25 Jul 2020 → 30 Jul 2020 Conference number: 43 https://sigir.org/sigir2020/ |
Publication series
Name | SIGIR '20 |
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Publisher | Association for Computing Machinery |
Conference
Conference | 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval |
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Abbreviated title | SIGIR 2020 |
Period | 25/07/20 → 30/07/20 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- joint context modeling
- weak supervision
- point-of-interest recommendation