A Quantum-Based Model for Interactive Information Retrieval

Even the best information retrieval model cannot always identify the most useful answers to a user query. This is in particular the case with web search systems, where it is known that users tend to minimise their effort to access relevant information. It is, however, believed that the interaction between users and a retrieval system, such as a web search engine, can be exploited to provide better answers to users. Interactive Information Retrieval (IR) systems, in which users access information through a series of interactions with the search system, are concerned with building models for IR, where interaction plays a central role. In this paper, we propose a general framework for interactive IR that is able to capture the full interaction process in a principled way. Our approach relies upon a generalisation of the probability framework of quantum physics.

[1]  Norbert Fuhr,et al.  A probability ranking principle for interactive information retrieval , 2008, Information Retrieval.

[2]  Sachi Arafat,et al.  Foundations research in information retrieval inspired by quantum theory , 2008 .

[3]  Hong Xie,et al.  Shifts of interactive intentions and information-seeking strategies in interactive information retrieval , 2000, Journal of the American Society for Information Science.

[4]  Massimo Melucci,et al.  A basis for information retrieval in context , 2008, TOIS.

[5]  Mark Magennis,et al.  The potential and actual effectiveness of interactive query expansion , 1997, SIGIR '97.

[6]  Karen Spärck Jones.  What's the value of TREC: is there a gap to jump or a chasm to bridge? , 2006, SIGF.

[7]  C. J. van Rijsbergen,et al.  Eraser Lattices and Semantic Contents , 2009, QI.

[8]  Dominic Widdows,et al.  Orthogonal Negation in Vector Spaces for Modelling Word-Meanings and Document Retrieval , 2003, ACL.

[9]  Stephen E. Robertson,et al.  Okapi/Keenbow at TREC-8 , 1999, TREC.

[10]  Yunjie Calvin Xu,et al.  Novelty and topicality in interactive information retrieval , 2008, J. Assoc. Inf. Sci. Technol..

[11]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[12]  Yunjie Calvin Xu,et al.  The dynamics of interactive information retrieval behavior, Part I: An activity theory perspective , 2007, J. Assoc. Inf. Sci. Technol..

[13]  Diederik Aerts,et al.  A Theory of Concepts and Their Combinations II: A Hilbert Space Representation , 2004 .

[14]  ChengXiang Zhai,et al.  A study of methods for negative relevance feedback , 2008, SIGIR '08.

[15]  ChengXiang Zhai,et al.  Implicit user modeling for personalized search , 2005, CIKM '05.

[16]  Ophir Frieder,et al.  Repeatable evaluation of search services in dynamic environments , 2007, TOIS.

[17]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[18]  C. J. van Rijsbergen,et al.  The geometry of information retrieval , 2004 .

[19]  Mounia Lalmas,et al.  Structured Information Retrieval and Quantum Theory , 2009, QI.

[20]  C. J. van Rijsbergen,et al.  Semantic Spaces: Measuring the Distance between Different Subspaces , 2009, QI.

[21]  Stephen E. Robertson,et al.  On the history of evaluation in IR , 2008, J. Inf. Sci..

[22]  Peter Ingwersen,et al.  The Turn - Integration of Information Seeking and Retrieval in Context , 2005, The Kluwer International Series on Information Retrieval.

[23]  C. J. van Rijsbergen,et al.  A Formalization of Logical Imaging for Information Retrieval Using Quantum Theory , 2008, 2008 19th International Workshop on Database and Expert Systems Applications.