The probability ranking principle in IR
Readings in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Term feedback for information retrieval with language models
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A comparative study of probabilistic and language models for information retrieval
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
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Relevance feedback algorithm is proposed to be an effective way to improve the precision of information retrieval. However, most researches about relevance feedback are based on vector space model, which can't be used in other more complicated and powerful models, such as language model and logic model. Meanwhile, other researches are conceptually restricted to the view of a query as a set of terms, and so cannot be naturally applied to more general case when the query is considered as a sequence of terms and the frequency information of a query tern is considered. In this paper, we mainly focuses on relevant feedback Algorithm based on language model. We use a mixture model to describe the process of generating document and use EM to solve model's parameters. Our research also employs semi-supervised learning to calculate collection model and proposes an effective way to obtain feedback from irrelevant documents to improve our algorithm.