Term weighting approaches for mining significant locations from personal location logs
In this paper, we describe experiments into the application of term weighting techniques from text retrieval to support the automatic identification of significant locations from a large location log, which we consider to be important for supporting many location-based social network applications. W...
| Main Authors: | , , , |
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| Format: | Conference item |
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2010
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| _version_ | 1797059156285325312 |
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| author | Qiu, Z Gurrin, C Doherty, A Smeaton, A |
| author_facet | Qiu, Z Gurrin, C Doherty, A Smeaton, A |
| author_sort | Qiu, Z |
| collection | OXFORD |
| description | In this paper, we describe experiments into the application of term weighting techniques from text retrieval to support the automatic identification of significant locations from a large location log, which we consider to be important for supporting many location-based social network applications. We identify the fact that the distribution of locations follows a similar shaped distribution to that of terms in a language and in so doing motivate our use of term weighting techniques. Using this information we then show that these proven techniques can be used to automatically identify social visits and "pass through" locations, as well as standard home and work locations. We also suggest that it is possible to classify whether an extended segment of personal location data may be a tourist trip, business trip or a typical working (at home) period of time. © 2010 IEEE. |
| first_indexed | 2024-03-06T20:00:10Z |
| format | Conference item |
| id | oxford-uuid:2704fd46-7b0a-43a7-80e8-96ce0ee806f8 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:00:10Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:2704fd46-7b0a-43a7-80e8-96ce0ee806f82022-03-26T12:04:26ZTerm weighting approaches for mining significant locations from personal location logsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:2704fd46-7b0a-43a7-80e8-96ce0ee806f8Symplectic Elements at Oxford2010Qiu, ZGurrin, CDoherty, ASmeaton, AIn this paper, we describe experiments into the application of term weighting techniques from text retrieval to support the automatic identification of significant locations from a large location log, which we consider to be important for supporting many location-based social network applications. We identify the fact that the distribution of locations follows a similar shaped distribution to that of terms in a language and in so doing motivate our use of term weighting techniques. Using this information we then show that these proven techniques can be used to automatically identify social visits and "pass through" locations, as well as standard home and work locations. We also suggest that it is possible to classify whether an extended segment of personal location data may be a tourist trip, business trip or a typical working (at home) period of time. © 2010 IEEE. |
| spellingShingle | Qiu, Z Gurrin, C Doherty, A Smeaton, A Term weighting approaches for mining significant locations from personal location logs |
| title | Term weighting approaches for mining significant locations from personal location logs |
| title_full | Term weighting approaches for mining significant locations from personal location logs |
| title_fullStr | Term weighting approaches for mining significant locations from personal location logs |
| title_full_unstemmed | Term weighting approaches for mining significant locations from personal location logs |
| title_short | Term weighting approaches for mining significant locations from personal location logs |
| title_sort | term weighting approaches for mining significant locations from personal location logs |
| work_keys_str_mv | AT qiuz termweightingapproachesforminingsignificantlocationsfrompersonallocationlogs AT gurrinc termweightingapproachesforminingsignificantlocationsfrompersonallocationlogs AT dohertya termweightingapproachesforminingsignificantlocationsfrompersonallocationlogs AT smeatona termweightingapproachesforminingsignificantlocationsfrompersonallocationlogs |