Context-based friend suggestion in online photo-sharing community
With the popularity of social media, web users tend to spend more time than before for sharing their experience and interest in online photo-sharing sites. The wide variety of sharing behaviors generate different metadata which pose new opportunities for the discovery of communities. We propose a ne...
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sg-smu-ink.sis_research-74982022-01-10T05:00:00Z Context-based friend suggestion in online photo-sharing community YAO, Ting NGO, Chong-wah MEI, Tao With the popularity of social media, web users tend to spend more time than before for sharing their experience and interest in online photo-sharing sites. The wide variety of sharing behaviors generate different metadata which pose new opportunities for the discovery of communities. We propose a new approach, named context-based friend suggestion, to leverage the diverse form of contextual cues for more effective friend suggestion in the social media community. Different from existing approaches, we consider both visual and geographical cues, and develop two user-based similarity measurements, i.e., visual similarity and geo similarity for characterizing user relationship. The problem of friend suggestion is casted as a contextual graph modeling problem, where users are nodes and the edges between them are weighted by geo similarity. Meanwhile, the graph is initialized in a way that users with higher visual similarity to a given query have better chance to be recommended. Experimental results on a dataset of 13,876 users and ∼1.5 million of their shared photos demonstrated that the proposed approach is consistent with human perception and outperforms other works. 2011-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6495 info:doi/10.1145/2072298.2071909 https://ink.library.smu.edu.sg/context/sis_research/article/7498/viewcontent/2072298.2071909.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Friend suggestion Social media User similarity Databases and Information Systems Graphics and Human Computer Interfaces Social Media |
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Friend suggestion Social media User similarity Databases and Information Systems Graphics and Human Computer Interfaces Social Media YAO, Ting NGO, Chong-wah MEI, Tao Context-based friend suggestion in online photo-sharing community |
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With the popularity of social media, web users tend to spend more time than before for sharing their experience and interest in online photo-sharing sites. The wide variety of sharing behaviors generate different metadata which pose new opportunities for the discovery of communities. We propose a new approach, named context-based friend suggestion, to leverage the diverse form of contextual cues for more effective friend suggestion in the social media community. Different from existing approaches, we consider both visual and geographical cues, and develop two user-based similarity measurements, i.e., visual similarity and geo similarity for characterizing user relationship. The problem of friend suggestion is casted as a contextual graph modeling problem, where users are nodes and the edges between them are weighted by geo similarity. Meanwhile, the graph is initialized in a way that users with higher visual similarity to a given query have better chance to be recommended. Experimental results on a dataset of 13,876 users and ∼1.5 million of their shared photos demonstrated that the proposed approach is consistent with human perception and outperforms other works. |
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YAO, Ting NGO, Chong-wah MEI, Tao |
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YAO, Ting NGO, Chong-wah MEI, Tao |
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YAO, Ting |
title |
Context-based friend suggestion in online photo-sharing community |
title_short |
Context-based friend suggestion in online photo-sharing community |
title_full |
Context-based friend suggestion in online photo-sharing community |
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Context-based friend suggestion in online photo-sharing community |
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Context-based friend suggestion in online photo-sharing community |
title_sort |
context-based friend suggestion in online photo-sharing community |
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Institutional Knowledge at Singapore Management University |
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2011 |
url |
https://ink.library.smu.edu.sg/sis_research/6495 https://ink.library.smu.edu.sg/context/sis_research/article/7498/viewcontent/2072298.2071909.pdf |
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