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- W2414470084 abstract "The advent of Twitter has led to the ubiquitous information overload problem with a dramatic increase in the amount of tweets a user is exposed to. In this paper, we consider real-time tweet filtering with respect to users' interest profiles in public Twitter stream. While traditional filtering methods mainly focus on judging relevance of a document, we aim to retrieve relevant and novel documents to address the high redundancy of tweets. An unsupervised approach is proposed to model relevance between tweets and different profiles adaptively and a neural network language model is employed to learn semantic representation for tweets. Experiments on TREC 2015 dataset demonstrate the effectiveness of the proposed approach." @default.
- W2414470084 created "2016-06-24" @default.
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- W2414470084 date "2016-06-19" @default.
- W2414470084 modified "2023-09-24" @default.
- W2414470084 title "Real-time Filtering on Interest Profiles in Twitter Stream" @default.
- W2414470084 cites W1931723352 @default.
- W2414470084 cites W2077916364 @default.
- W2414470084 doi "https://doi.org/10.1145/2910896.2925462" @default.
- W2414470084 hasPublicationYear "2016" @default.
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