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Multiple feature fusion for social media applications

Published: 06 June 2010 Publication History

Abstract

The emergence of social media as a crucial paradigm has posed new challenges to the research and industry communities, where media are designed to be disseminated through social interaction. Recent literature has noted the generality of multiple features in the social media environment, such as textual, visual and user information. However, most of the studies employ only a relatively simple mechanism to merge the features rather than fully exploit feature correlation for social media applications. In this paper, we propose a novel approach to fusing multiple features and their correlations for similarity evaluation. Specifically, we first build a Feature Interaction Graph (FIG) by taking features as nodes and the correlations between them as edges. Then, we employ a probabilistic model based on Markov Random Field to describe the graph for similarity measure between multimedia objects. Using that, we design an efficient retrieval algorithm for large social media data. Further, we integrate temporal information into the probabilistic model for social media recommendation. We evaluate our approach using a large real-life corpus collected from Flickr, and the experimental results indicate the superiority of our proposed method over state-of-the-art techniques.

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      cover image ACM Conferences
      SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
      June 2010
      1286 pages
      ISBN:9781450300322
      DOI:10.1145/1807167
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      Published: 06 June 2010

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      Author Tags

      1. feature fusion
      2. recommendation
      3. search
      4. social media

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      SIGMOD/PODS '10: International Conference on Management of Data
      June 6 - 10, 2010
      Indiana, Indianapolis, USA

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      • (2023)The Impact of Social Media on Online Shopping Behavior of Gen Z Consumers In Time of Covid-19 Pandemic; The Moderating Role of Celebrity EndorsementsWSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS10.37394/23207.2024.21.2421(266-279)Online publication date: 8-Dec-2023
      • (2022)Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers’ perspectivesHeliyon10.1016/j.heliyon.2022.e10600(e10600)Online publication date: Sep-2022
      • (2020)Recommender Systems Leveraging Multimedia ContentACM Computing Surveys10.1145/340719053:5(1-38)Online publication date: 28-Sep-2020
      • (2020)Personalized Video Recommendation Using Rich Contents from VideosIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288552032:3(492-505)Online publication date: 7-Feb-2020
      • (2020)Artificial intelligence for clinical decision support in neurologyBrain Communications10.1093/braincomms/fcaa0962:2Online publication date: 9-Jul-2020
      • (2019)IR Feature Embedded BOF Indexing Method for Near-Duplicate Video RetrievalIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.288494129:12(3743-3753)Online publication date: Dec-2019
      • (2019)Global-view hashingWorld Wide Web10.1007/s11280-018-0536-722:2(771-789)Online publication date: 1-Mar-2019
      • (2019)Real-time context-aware social media recommendationThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0524-728:2(197-219)Online publication date: 1-Apr-2019
      • (2019)Cross-Modal Multimedia Archives Information Retrieval Based on Semantic MatchingAdvances in Intelligent Systems and Interactive Applications10.1007/978-3-030-34387-3_50(407-413)Online publication date: 30-Nov-2019
      • (2018)A Contextual Attention Recurrent Architecture for Context-Aware Venue RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210042(555-564)Online publication date: 27-Jun-2018
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