An efficient indexing for top-k query answering in location-based recommendation system
Location-based recommendation systems are obtaining interests from both the business and research communities recently. In this paper, we propose an indexing approach to improve the efficiency of the top-k query answering for a prominent location-based recommendation model, User-centered collaborati...
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| Main Authors: | , |
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| Format: | Conference Proceeding |
| Published: |
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904490800&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45350 |
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| Institution: | Chiang Mai University |
| Summary: | Location-based recommendation systems are obtaining interests from both the business and research communities recently. In this paper, we propose an indexing approach to improve the efficiency of the top-k query answering for a prominent location-based recommendation model, User-centered collaborative location and activity filtering (UCLAF). The efficiency issue of such query type is important since there could be enormous users, locations, and activities in the recommendation model. When a query is issued, not all of the answers are to be obtained, but only a few most-relevant answers. Our proposed work is based on a multi-dimensional index, aR-Tree. A feature of such index tree, i.e. only-relevant information traversal, is utilized with some modification. In addition, the experiments have been conducted to evaluate our proposed work. In which, the results, which our work is compared with a few indexing methods, show that our work is highly efficient when system is scaled up. © 2014 IEEE. |
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