By Konstantinos Chorianopoulos, David A. Shamma, Lyndon Kennedy (auth.), Naeem Ramzan, Roelof van Zwol, Jong-Seok Lee, Kai Clüver, Xian-Sheng Hua (eds.)
ISBN-10: 1447145542
ISBN-13: 9781447145547
ISBN-10: 1447145550
ISBN-13: 9781447145554
This finished text/reference examines intensive the synergy among multimedia content material research, personalization, and next-generation networking. The e-book demonstrates how this integration can lead to powerful, customized companies that supply clients with a much better multimedia-centric caliber of expertise. every one bankruptcy deals a realistic step by step walkthrough for numerous ideas, parts and applied sciences with regards to the improvement of functions and companies. subject matters and lines: introduces the basics of social media retrieval, providing an important components of study during this area; examines the $64000 subject of multimedia tagging in social environments, together with geo-tagging; discusses problems with personalization and privateness in social media; studies advances in encoding, compression and community architectures for the trade of social media info; describes quite a number functions regarding social media.
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Extra info for Social Media Retrieval
Sample text
In this recommendation, the relevance between a user and a content is to be evaluated, so that contents that can mostly interest a user are recommended. The recommendation is based on the user relations and actions and the content similarity. ” which can be extended into two information retrieval scenarios: (1) ranking of users, given a content item, who should share it so that its diffusion range can be maximized in a social network; and (2) ranking of social media contents, given a user, what should one share to maximize one’s influence among one’s friends.
We regard the factors that affect users’ overall social influence (excluding the contents) as the user-specific factors. • Content-specific factors. Similar as user-specific factors, we regard the factors affecting contents’ overall social influence (excluding the users) as the contentspecific factors. • User-content-specific factors. As mentioned above, the social influence is usercontent specific. The social influence of a user given a content cannot be well estimated only by the user and content-specific factors.
In the joint social-content recommendation, (a) and (b) can be solved by updating the user-content matrix, based on the original matrices A, B, and C, where both users’ social connections and the content similarities can be used to assist the updating of the missing entries in B. In the joint social-content recommendation, users’ social connections, user actions, and content similarities are utilized to derive an updated user-content graph to perform joint recommendation. More specifically, first, the joint social-content recommendation utilizes a user-content matrix completion to predict which contents are to be imported/shared by which users.
Social Media Retrieval by Konstantinos Chorianopoulos, David A. Shamma, Lyndon Kennedy (auth.), Naeem Ramzan, Roelof van Zwol, Jong-Seok Lee, Kai Clüver, Xian-Sheng Hua (eds.)
by Richard
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