Learning Hidden Variable Models For Blog Retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval(2010)
摘要
We describe probabilistic models that leverage individual blog post evidence to improve blog seed retrieval performances. Our model offers a intuitive and principled method to combine multiple posts in scoring a whole blog site by treating individual posts as hidden variables. When applied to the seed retrieval task, our model yields state-of-the-art results on the TREC 2007 Blog Distillation Task dataset.
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关键词
Learning to Rank,Passage Retrieval,Blog Retrieval
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