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Conference Paper: Location-sensitive resources recommendation in social tagging systems
Title | Location-sensitive resources recommendation in social tagging systems |
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Authors | |
Keywords | Location-sensitive Ranking Resources recommendation Location information Location-sensitive |
Issue Date | 2012 |
Publisher | ACM. |
Citation | The 21st ACM Conference on Information and Knowledge Management (CIKM'12), Maui, HI., 29 October-2 November 2012. In Proceedings of the 21st ACM CIKM, 2012, p. 1960-1964 How to Cite? |
Abstract | In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource's tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods. © 2012 ACM. |
Persistent Identifier | http://hdl.handle.net/10722/164912 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Wan, C | en_US |
dc.contributor.author | Kao, B | en_US |
dc.contributor.author | Cheung, DWL | en_US |
dc.date.accessioned | 2012-09-20T08:12:21Z | - |
dc.date.available | 2012-09-20T08:12:21Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | The 21st ACM Conference on Information and Knowledge Management (CIKM'12), Maui, HI., 29 October-2 November 2012. In Proceedings of the 21st ACM CIKM, 2012, p. 1960-1964 | en_US |
dc.identifier.isbn | 978-1-4503-1156-4 | - |
dc.identifier.uri | http://hdl.handle.net/10722/164912 | - |
dc.description.abstract | In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource's tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods. © 2012 ACM. | - |
dc.language | eng | en_US |
dc.publisher | ACM. | - |
dc.relation.ispartof | Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM'12 | en_US |
dc.subject | Location-sensitive | - |
dc.subject | Ranking | - |
dc.subject | Resources recommendation | - |
dc.subject | Location information | - |
dc.subject | Location-sensitive | - |
dc.title | Location-sensitive resources recommendation in social tagging systems | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wan, C: cwan@cs.hku.hk | en_US |
dc.identifier.email | Kao, B: kao@cs.hku.hk | en_US |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | - |
dc.identifier.authority | Kao, B=rp00123 | en_US |
dc.identifier.authority | Cheung, DWL=rp00101 | en_US |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1145/2396761.2398552 | - |
dc.identifier.scopus | eid_2-s2.0-84871093001 | - |
dc.identifier.hkuros | 207196 | en_US |
dc.identifier.spage | 1960 | - |
dc.identifier.epage | 1964 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 130417 | - |