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IEEE Transactions on Knowledge and Data Engineering
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ACM Transactions on Database Systems (TODS)
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Journal of the American Society for Information Science and Technology
User modeling for personalized Web search with self-organizing map: Research Articles
Journal of the American Society for Information Science and Technology
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Extracting user profiles from large scale data
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Natural Computing: an international journal
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A statistical approach to mining customers' conversational data from social media
IBM Journal of Research and Development
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Push-based data delivery requires knowledge of user interests for making scheduling, bandwidth allocation, and routing decisions. Such information is maintained as user profiles. We propose a new incremental algorithm for constructing user profiles based on monitoring and user feedback. In contrast to earlier approaches, which typically represent profiles as a single weighted interest vector, we represent user profiles as multiple interest vectors, whose number, size, and elements change adaptively based on user access behavior. This flexible approach allows the profile to more accurately represent complex user interests. Although there has been significant research on user profiles, our approach is unique in that it can be tuned to trade off profile complexity and quality. This feature, together with its incremental nature, makes our method suitable for use in large-scale information filtering applications such as push-based WWW page dissemination. We evaluate the method by experimentally investigating its ability to categorize WWW pages taken from Yahoo! categories. Our results show that the method can provide high filtering effectiveness with modest profile sizes and can effectively adapt to changes in users' interests.