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LBSNRank: personalized pagerank on location-based social networks

Published: 05 September 2012 Publication History

Abstract

Different from traditional social networks, the location-based social networks allow people to share their locations according to location-tagged user-generated contents, such as checkins, trajectories, text, photos, etc. In location-based social networks, which are based on users' checkins, people could share his or her location according to checkin while visiting around. However, people's locations change frequently and the rankings of people change dynamically too, which makes ranking on graphs a challenging work. To address this challenge, we propose the LBSNRank algorithm on graphs with nodes whose contents change dynamically. To validate our algorithm on real datasets, we have crawled and analyzed a dataset from the Dianping website. Experiments on this real dataset show that our LBSNRank algorithm performs better than traditional personalized PageRank in efficiency.

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cover image ACM Conferences
UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing
September 2012
1268 pages
ISBN:9781450312240
DOI:10.1145/2370216
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 September 2012

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Author Tags

  1. Monte Carlo method
  2. location-based social networks
  3. mapreduce
  4. personalized pagerank
  5. random walk

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Ubicomp '12
Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing
September 5 - 8, 2012
Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;
Overall Acceptance Rate 764 of 2,912 submissions, 26%

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  • (2019)Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRankTheory and Applications of Models of Computation10.1007/978-3-030-14812-6_25(410-423)Online publication date: 6-Mar-2019
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