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
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Using the quantum probability ranking principle to rank interdependent documents
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Has portfolio theory got any principles?
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
An analysis of ranking principles and retrieval strategies
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
On the use of complex numbers in quantum models for information retrieval
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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The Quantum Probability Ranking Principle (QPRP) has been recently proposed, and accounts for interdependent document relevance when ranking. However, to be instantiated, the QPRP requires a method to approximate the "interference" between two documents. In this poster, we empirically evaluate a number of different methods of approximation on two TREC test collections for subtopic retrieval. It is shown that these approximations can lead to significantly better retrieval performance over the state of the art.