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Mining city landmarks from blogs by graph modeling

Published: 19 October 2009 Publication History

Abstract

Recent years have witnessed great prosperity in community-contributed multimedia. Discovering, extracting, and summarizing knowledge from these data enables us to make better sense of the world. In this paper, we report our work on mining famous city landmarks from blogs for personalized tourist suggestions. Our main contribution is a graph modeling framework to discover city landmarks by mining blog photo correlations with community supervision. This modeling fuses context, content, and community information in a style that simulates both static (PageRank) and dynamic (HITS) ranking models to highlight representative data from the consensus of blog users.
Preliminary, we identify geographical locations of page contents to harvest city sight photos from Web blogs, based on which we structure these photos into a Scene-View hierarchy* within each city. Our graph modeling consists of two phases: First, within a given scene, we present a PhotoRank algorithm to discover its representative views, which analogizes PageRank to model context and content photo correlations for graph-based popularity propagation. Second, among scenes within each city, we present a Landmark-HITS model to discover city landmarks, which integrates author correlations to infer scene popularity in a semi-supervised reinforcement manner. Based on graph modeling, we further achieve personalized tourist suggestions by the collaborative filtering of tourism logs and author correlations. Based on a real-world dataset from Windows Live Spaces blogs containing nearly 400,000 sight photos, we have deployed our framework in a VisualTourism system, with comparisons to state-of-the-arts. We also investigate how the city popularities, user locations (e.g. Asian or Euro. blog users), and sequential events (e.g. Olympic Games) influence our Landmark discovery results and the tourist suggestion tendencies.

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cover image ACM Conferences
MM '09: Proceedings of the 17th ACM international conference on Multimedia
October 2009
1202 pages
ISBN:9781605586083
DOI:10.1145/1631272
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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Publication History

Published: 19 October 2009

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Author Tags

  1. HITS
  2. PageRank
  3. collaborative filtering
  4. landmark discovery
  5. location extraction
  6. tourist suggestion
  7. user study
  8. web blog

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MM09
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MM09: ACM Multimedia Conference
October 19 - 24, 2009
Beijing, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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  • (2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Mar-2024
  • (2023)Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00300(2986-2996)Online publication date: Jan-2023
  • (2023)Constructing a personalized travel itinerary recommender system with the Internet of ThingsWireless Networks10.1007/s11276-023-03453-yOnline publication date: 23-Aug-2023
  • (2022)Efficient itinerary recommendation via personalized POI selection and pruningKnowledge and Information Systems10.1007/s10115-021-01648-364:4(963-993)Online publication date: 2-Mar-2022
  • (2021)City size based scaling of the urban internal nodes layoutPLOS ONE10.1371/journal.pone.025034816:4(e0250348)Online publication date: 23-Apr-2021
  • (2021)Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNsIEEE Access10.1109/ACCESS.2020.30476809(3140-3154)Online publication date: 2021
  • (2020)Fashion Compatibility Modeling through a Multi-modal Try-on-guided SchemeProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401047(771-780)Online publication date: 25-Jul-2020
  • (2020)Perceiving Beijing’s “City Image” Across Different Groups Based on Geotagged Social Media DataIEEE Access10.1109/ACCESS.2020.29950668(93868-93881)Online publication date: 2020
  • (2019)Compatibility Modeling: Data and Knowledge Applications for Clothing MatchingSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00952ED1V01Y201909ICR06911:3(1-138)Online publication date: 2-Oct-2019
  • (2019)Tourism application with CNN-Based Classification specialized for cultural informationProceedings of the 21st International Conference on Information Integration and Web-based Applications & Services10.1145/3366030.3366073(8-14)Online publication date: 2-Dec-2019
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