Personalized Advertising Strategy for Integrated Social Networking Websites

In addition to provide major funding for many Internet companies, online advertising creates a disutility to consumers, subsequently reducing market share. However, previous works focus only on the topical relevance of ads and, in doing so, neglect consumer attitudes. From the view of text processin...

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Bibliographic Details
Published in2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Vol. 3; pp. 369 - 372
Main Authors Hsieh, Chang-Tai, Liang, Chun-Ming, Chou, Shih-Chun
Format Conference Proceeding
LanguageEnglish
Published Washington, DC, USA IEEE Computer Society 09.12.2008
IEEE
SeriesACM Conferences
Subjects
Advertising
advertisment strategy
Applied computing > Enterprise computing
Applied computing > Operations research > Marketing
Avatars
Buildings
Games
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction
Information services
Information systems > Information retrieval
Information systems > Information storage systems
Internet
Social network
user preference mining
Web sites
advertisment strategy
user preference mining
Social network
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Summary:In addition to provide major funding for many Internet companies, online advertising creates a disutility to consumers, subsequently reducing market share. However, previous works focus only on the topical relevance of ads and, in doing so, neglect consumer attitudes. From the view of text processing, they focus only on the topic dimension of texts, while paying no attention to the sentiment dimension. This work proposes a feature extraction process to match advertisement and targeted users by extracting features from the user’s profile and advertisement specification. First, the proposed platform relies on mine characteristics supplied by a user to his avatar including preferred color, style and feeling. Second, the system selects the best matching advertisement based on the user’s variable interests (as expressed on his blog). These features are scored and finally these advertisements are conveyed to the target users by product. Experimental results in several topics demonstrate that the proposed framework works well in detecting a user’s potential preferences, and in recommending suitable advertisements
ISBN:9780769534961
0769534961
DOI:10.1109/WIIAT.2008.156