Monetizing User Activity on Social Networks - Challenges and Experiences

This work summarizes challenges and experiences in monetizing user activity on public forums on social network sites. We present a approach that identifies the monetization potential of user posts and eliminates off-topic content to identify the most relevant and monetizable keywords for advertising...

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Veröffentlicht in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Jg. 1; S. 92 - 99
Hauptverfasser: Nagarajan, Meenakshi, Baid, Kamal, Sheth, Amit, Wang, Shaojun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Advertising
Classification algorithms
Computing methodologies > Machine learning
Consumer electronics
contentextual ads
Information sharing
Information systems > Information retrieval > Evaluation of retrieval results
Information systems > Information retrieval > Users and interactive retrieval > Personalization
Information systems > Information storage systems > Storage architectures > Storage network architectures
Information systems > World Wide Web > Web searching and information discovery > Personalization
intents
Navigation
Noise
off-topic noise
Privacy
Social networking (online)
social networks
Training
user activity
Web search
off-topic noise
intents
user activity
contentextual ads
social networks
ISBN:0769538010, 9780769538013
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This work summarizes challenges and experiences in monetizing user activity on public forums on social network sites. We present a approach that identifies the monetization potential of user posts and eliminates off-topic content to identify the most relevant and monetizable keywords for advertising. Preliminary studies using data from MySpace and Facebook show that 52% of ad impressions generated using keywords from our system were more targeted compared to the 30% relevant impressions generated without using our system.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.20