Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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
The use of MMR, diversity-based reranking for reordering documents and producing summaries
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
Modern Information Retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Bayesian extension to the language model for ad hoc information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Risky business: modeling and exploiting uncertainty in information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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In this paper we present a learning algorithm to estimate a risksensitive and document-relation embedded ranking function so that the ranking score can reflect both the query-document relevance degree and the risk of estimating relevance when the document relation is considered. With proper assumptions, an analytic form of the ranking function is attainable with a ranking score being a linear combination among the expectation of relevance score, the variance of relevance estimation and the covariance with the other documents. We provide a systematic framework to study the roles of the relevance, the variance and the covariance in ranking documents and their relations with the different performance metrics. The experiments show that incorporating the variance in ranking score improves both the relevance and diversity.