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Recommending Flickr groups with social topic model

Published: 01 June 2012 Publication History

Abstract

The explosion of multimedia content in social media networks raises a great demand of developing tools to facilitate producing, sharing and viewing media content. Flickr groups, self-organized communities with declared common interests, are able to help users to conveniently participate in social media network. In this paper, we address the problem of automatically recommending groups to users. We propose to simultaneously exploit media contents and link structures between users and groups. To this end, we present a probabilistic latent topic model to model them in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function. We demonstrate the proposed approach on the dataset crawled from Flickr.com.

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  • (2023)Dual Intents Graph Modeling for User-centric Group DiscoveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614855(2716-2725)Online publication date: 21-Oct-2023
  • (2023)Ranking-based Group Identification via Factorized Attention on Social Tripartite GraphProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570406(769-777)Online publication date: 27-Feb-2023
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      Reviews

      Alyx Macfadyen

      Social networking is crucial for developing networks of common interest, but it is important to remember there may be costs involved. In this paper, the deployment of the Flickr application programming interface (API) is discussed. Wang et al. present a probabilistic model intended to uncover latent topics of interest among Flickr users and groups. The purpose is to improve the recommendation of groups to users and users to groups. Probability models are useful for developing inferences from large datasets, and are frequently used in decision support systems. The authors list related work, and describe their own model as "a hybrid approach [that] exploits both visual contents and the existing links between users and Flickr groups." The initial phase of this work seeks to discover common topics of interest among users and groups, the claim being that the results yield a more consistent dataset. To that end, the authors undertook an examination of visual and textual features using a vocabulary of visual words [1] in conjunction with the textual tags associated with each image in the dataset. Visual data generates large amounts of overhead, and the analysis of visual data is complex. The comparison of the published results of this study with other methods of group recommendation discussed here suggests only an incremental improvement in topic/user/group matching. Although this work is interesting, I am not convinced the authors have made substantial progress in predicting latent or other Flickr topics. However, further work may yield results that are more promising. Online Computing Reviews Service

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      Information & Contributors

      Information

      Published In

      cover image Information Retrieval
      Information Retrieval  Volume 15, Issue 3-4
      Jun 2012
      233 pages

      Publisher

      Kluwer Academic Publishers

      United States

      Publication History

      Published: 01 June 2012
      Accepted: 15 February 2012
      Received: 29 April 2011

      Author Tags

      1. Flickr group
      2. Recommendation
      3. Social topic model

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      • (2023)Dual Intents Graph Modeling for User-centric Group DiscoveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614855(2716-2725)Online publication date: 21-Oct-2023
      • (2023)Ranking-based Group Identification via Factorized Attention on Social Tripartite GraphProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570406(769-777)Online publication date: 27-Feb-2023
      • (2022)Relation-Aware Compositional Zero-Shot Learning for Attribute-Object Pair RecognitionIEEE Transactions on Multimedia10.1109/TMM.2021.310441124(3652-3664)Online publication date: 1-Jan-2022
      • (2022)Multi-Modal Meta Multi-Task Learning for Social Media Rumor DetectionIEEE Transactions on Multimedia10.1109/TMM.2021.306549824(1449-1459)Online publication date: 1-Jan-2022
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      • (2021)Cross-Modal Knowledge Adaptation for Language-Based Person SearchIEEE Transactions on Image Processing10.1109/TIP.2021.306882530(4057-4069)Online publication date: 1-Jan-2021
      • (2020)Cross-Entropy Adversarial View Adaptation for Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.290954930:7(2081-2092)Online publication date: 1-Jul-2020
      • (2019)Unsupervised and Semi-Supervised Image Classification With Weak Semantic ConsistencyIEEE Transactions on Multimedia10.1109/TMM.2019.290362821:10(2482-2491)Online publication date: 1-Oct-2019
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