@conference {2012,
	title = {User Interests Driven Web Personalization Based on Multiple Social Networks},
	booktitle = {Proceedings of the 4th International Workshop on Web Intelligence \&\#38; Communities},
	year = {2012},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {User related data indicate user interests in a certain environment. In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.},
	keywords = {interest analysis, search refinement, web personalization},
	isbn = {978-1-4503-1189-2},
	doi = {10.1145/2189736.2189749},
	url = {http://doi.acm.org/10.1145/2189736.2189749},
	author = {Zeng, Yi and Zhong, Ning and Ren, Xu and Wang, Yan}
}

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In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.","keywords":"interest analysis, search refinement, web personalization","isbn":"978-1-4503-1189-2","doi":"10.1145/2189736.2189749","url":"http://doi.acm.org/10.1145/2189736.2189749","author":[{"propositions":[],"lastnames":["Zeng"],"firstnames":["Yi"],"suffixes":[]},{"propositions":[],"lastnames":["Zhong"],"firstnames":["Ning"],"suffixes":[]},{"propositions":[],"lastnames":["Ren"],"firstnames":["Xu"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Yan"],"suffixes":[]}],"bibtex":"@conference {2012,\n\ttitle = {User Interests Driven Web Personalization Based on Multiple Social Networks},\n\tbooktitle = {Proceedings of the 4th International Workshop on Web Intelligence \\&\\#38; Communities},\n\tyear = {2012},\n\tpublisher = {ACM},\n\torganization = {ACM},\n\taddress = {New York, NY, USA},\n\tabstract = {User related data indicate user interests in a certain environment. In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.},\n\tkeywords = {interest analysis, search refinement, web personalization},\n\tisbn = {978-1-4503-1189-2},\n\tdoi = {10.1145/2189736.2189749},\n\turl = {http://doi.acm.org/10.1145/2189736.2189749},\n\tauthor = {Zeng, Yi and Zhong, Ning and Ren, Xu and Wang, Yan}\n}\n","author_short":["Zeng, Y.","Zhong, N.","Ren, X.","Wang, Y."],"key":"2012","id":"2012","bibbaseid":"zeng-zhong-ren-wang-userinterestsdrivenwebpersonalizationbasedonmultiplesocialnetworks-2012","role":"author","urls":{"Paper":"http://doi.acm.org/10.1145/2189736.2189749"},"keyword":["interest analysis","search refinement","web personalization"],"downloads":0},"search_terms":["user","interests","driven","web","personalization","based","multiple","social","networks","zeng","zhong","ren","wang"],"keywords":["interest analysis","search refinement","web personalization"],"authorIDs":[],"dataSources":["FWsPTwsmjtrBtRS3B"]}