Quantum probabilities are a formalism based on probability, logic and geometry – three ingredients used by most models of Information Access (IA). This led van Rijsbergen to suggest their use in IA [Rij04] because, unlike “standard” probabilities based on set theory, the “quantum” ones are based on vector spaces (Hilbert spaces) and are therefore more expressive.

This flexibility has begun to be exploited in order to tackle old and new challenges in IA, such as ad-hoc information retrieval (IR) [PF 10], contextual IR [Mel08], the problem of diversifying search results [ZA10], and summarization [PA 12]. In most of these studies, information objects are represented in a multi-dimensional, ie a set of vectors (probability density) or a subspace (event).

A crucial point for the success of quantum AI is to quickly find information objects. However, there is currently no effective technique for finding objects represented in Hilbert spaces directly, without first substantially reducing the number of objects considered with standard techniques. This limits the practical importance of such models and introduces a bias in the obtained results. It is necessary to design data structures and access methods adapted to the problem of quantum AI.

Objectives

This is a six months long internship (starting March-October 2012) and will take place in the LIP6/CNRS laboratory in Paris, France. The monthly allowance is of 420 €.

The theoretical objective of the INDEQ project is to develop index structures for fast object retrieval, when objects are represented in Hilbert spaces. To achieve this goal, tree-based indices will be developed that estimate at each node what is the probability distribution of objects in each branch (given a quantum probability distribution).

The practical objective of this project is to design a solution to recommend information objects (e.g., movies) to a user, while avoiding exploring exhaustively all the candidate objects. To this end, the project aims at defining a new access method for selecting the object or objects that have the highest probability to match the user requesting a recommendation.

Contact Information

Please email us if you are interested by this internship.

Benjamin Piwowarski – benjamin@bpiwowar.net
Hubert Naacke – hubert.naacke@lip6.fr

Bibliography

[Mel08] Melucci, M. (2008). A basis for information retrieval in context. ACM Transactions On Information Systems, 26(3), 1–41.

[PF+10] Piwowarski, B., Frommholz, I., Lalmas, M., & van Rijsbergen, K. (2010). What can Quantum Theory bring to IR In J. Huang, N. Koudas, G. Jones, X. Wu, K. Collins-Thompson, & A. An (Eds.), CIKM’10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management.

[PA+12] Piwowarski, B., Amini, M.-R., & Lalmas, M. (2012). On using a Quantum Physics formalism for Multi-document Summarisation. Journal of the American Society for Information Science and Technology (accepted paper to be published).

[Rij04] van Rijsbergen, K. (2004). The Geometry of Information Retrieval. Cambridge University Press.

[ZA10] Zuccon, G., & Azzopardi, L. (2010). Using the Quantum Probability Ranking Principle to Rank Interdependent Documents. In C. Gurrin, Y. He, G. Kazai, U. Kruschwitz, S. Little, T. Roelleke, S. Rüger, et al. (Eds.), Advances in Information Retrieval (Vol. 5993, pp. 357–369). Springer.

• B. Piwowarski, M. Amini, and M. Lalmas, “On using a Quantum Physics formalism for Multi-document Summarisation,” {Journal of the American Society for Information Science and Technology}.
  [Bibtex]

   @Article{Piwowarski2011OnUsing, author = {Piwowarski, Benjamin and Amini, Massih-Reza and Lalmas, Mounia}, title = {{On using a Quantum Physics formalism for Multi-document Summarisation}}, journal = {{Journal of the American Society for Information Science and Technology}}, note = {accepted for publication} }

It will be hopefully be followed by a paper about the quantum information framework (QIA) in all its generality (along with the code for computing “kernel quantum probabilities”).

1. the first time was done by K. van Rijsbergen for the vectorial models

The first one, a poster in ECIR 2011, deals with how to cope with the representation of the information need when the user reformulates the query and propose some heuristics (no evaluation yet):

• I. Frommholz, B. Piwowarski, M. Lalmas, and K. van Rijsbergen, “Processing queries in session in a quantum-inspired ir framework,” in Proceedings of ECIR 2011, 2011.
  [Bibtex]

   @inproceedings{Frommholz2011Processing, Author = {Ingo Frommholz and Benjamin Piwowarski and Mounia Lalmas and Keith van Rijsbergen}, Booktitle = {Proceedings of {ECIR} 2011}, Date-Added = {2011-01-03 16:56:38 +0000}, Date-Modified = {2011-04-11 13:31:23 +0100}, Month = {March}, Note = {Poster}, Title = {Processing Queries in Session in a Quantum-inspired IR Framework}, Year = {2011}}

The second one, a paper in the Italian IR 2011 workshop presents the algebra for information need that can be used to build structured query representations. The algebra is based on quantum operators that operates on so-called “information need aspects”. In the paper, we propose to use computational linguistics tools (segmentation, dependency parsing) in order to build automatically an algebraic repreesentation of the topic at hand.

• A. Caputo, B. Piwowarski, and M. Lalmas, “A query algebra for quantum information retrieval,” in Proceedings of the 2nd italian information retrieval workshop, 2011.
  [Bibtex]

   @inproceedings{Caputo2011QueryAlgebra, Author = {Annalina Caputo and Benjamin Piwowarski and Mounia Lalmas}, Booktitle = {Proceedings of the 2nd Italian Information Retrieval Workshop}, Date-Added = {2011-01-03 16:59:26 +0000}, Date-Modified = {2011-04-11 13:31:25 +0100}, Month = {January}, Title = {A Query Algebra for Quantum Information Retrieval}, Year = {2011}}

• S. H. Yang and H. Zha, “Language pyramid and multi-scale text analysis,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Yang2010Language, Author = {Shuang Hong Yang and Hongyuan Zha}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:45:21 +0000}, Date-Modified = {2010-11-02 11:16:44 +0000}, Doi = {10.1145/1871437.1871520}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Language Pyramid and Multi-Scale Text Analysis}, Year = {2010}}

Extends the BOW representation by using multi-resolution techniques from the image processing community. Basically, each text is first represented as a binary 2D matrix (term / position). Using spatial and semantic smoothing, this matrix can be blurred at different levels (until it becomes a matrix with just one value) which gives a multi-resolution text representation. Some experiments are performed for classification and retrieval.

• R. Cummins, M. Lalmas, and C. O’Riordan, “Examining the information retrieval process from an inductive perspective,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Cummins2010Examining, Author = {Ronan Cummins and Mounia Lalmas and Colm O'Riordan}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:01:59 +0000}, Date-Modified = {2010-11-02 11:16:44 +0000}, Doi = {10.1145/1871437.1871453}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Examining the Information Retrieval Process from an Inductive Perspective}, Year = {2010}}

Looks at what percentage of the information from the three usual sources (collection, document, and query) is necessary for IR models to perform well.

• F. Raiber and O. Kurland, “On identifying representative relevant documents,” , 2010.
  [Bibtex]

   @inproceedings{Raiber2010IdentifyingRepresentative, Author = {Fiana Raiber and Oren Kurland}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-02 11:00:55 +0000}, Date-Modified = {2010-11-02 11:16:43 +0000}, Doi = {10.1145/1871437.1871454}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {On Identifying Representative Relevant Documents}, Year = {2010}}

Evaluates different strategies to select relevant documents for relevance feedback for BM25 and Language Models.

• L. Zhao and J. Callan, “Term necessity prediction,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Zhao2010TermNecessity, Author = {Le Zhao and Jamie Callan}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:04:49 +0000}, Date-Modified = {2010-11-02 11:16:43 +0000}, Doi = {10.1145/1871437.1871474}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Term Necessity Prediction}, Year = {2010}}

Explores how necessary a term is for a document to be relevant – i.e. P(term in document | document is relevant to q). This probability is ignored in most models – and proposes machine learning based techniques to estimate this probability for query features.

• K. Min, Z. Zhang, J. Wright, and Y. Ma, “Decomposing background topics from keywords by principal component pursuit,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Min2010Decomposing, Author = {Kerui Min and Zhengdong Zhang and John Wright and Yi Ma}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:20:05 +0000}, Date-Modified = {2010-11-02 11:16:44 +0000}, Doi = {10.1145/1871437.1871475}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Decomposing Background Topics from Keywords by Principal Component Pursuit}, Year = {2010}}

An interesting extension to LSI, where documents are represented both by a reduced representation (given by LSI) and by a additive term that captures salient features of this document (e.g. low frequency terms).

• I. B. Hauskrecht, “Constructing classication features using minimal predictive patterns,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Hauskrecht2010Constructing, Author = {Iyad BatalMilos Hauskrecht}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:43:19 +0000}, Date-Modified = {2010-11-02 11:16:44 +0000}, Doi = {10.1145/1871437.1871549}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Constructing Classication Features Using Minimal Predictive Patterns}, Year = {2010}}

Explores how to do feature based selection with mining frequent term sets strategies. Shows substantial improvement over other methods.

• K. Punera and S. Merugu, “The anatomy of a click: modeling user behavior on webinformation systems,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Punera2010The-Anatomy, Author = {Kunal Punera and Srujana Merugu}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:47:51 +0000}, Date-Modified = {2010-11-02 11:16:43 +0000}, Doi = {10.1145/1871437.1871563}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {The Anatomy of a Click: Modeling User Behavior on WebInformation Systems}, Year = {2010}}

Models observed and unobserved behaviour of the user on a SERP, using a HMM-like model where a state is a vector of random variables (that can be observed or not) and the output is the observed (click) or unobserved (e.g. explores next/previous document) user action.

• C. Hauff, L. Azzopardi, and D. Kelly, “A comparison of user and system query performance predictions,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Hauff2010A-Comparison, Author = {Claudia Hauff and Leif Azzopardi and Diane Kelly}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:44:12 +0000}, Date-Modified = {2010-11-02 11:16:43 +0000}, Doi = {10.1145/1871437.1871562}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {A Comparison of User and System Query Performance Predictions}, Year = {2010}}

Compares how different are query difficulty predictors from what users would say.

• R. W. White, P. N. Bennett, and S. T. Dumais, “Predicting short-term interests using activity-based search context,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{White2010Predicting, Author = {Ryen W. White and Paul N. Bennett and Susan T. Dumais}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Date-Added = {2010-11-01 15:47:02 +0000}, Date-Modified = {2010-11-02 11:16:44 +0000}, Doi = {10.1145/1871437.1871565}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {Predicting Short-Term Interests Using Activity-Based Search Context}, Year = {2010}}

Looks at how to predict how much the context of a query (i.e. queries in the current session) is to be taken into account. Uses a parametrized mixture of language models from the context and the query.

Abtract: The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.

• B. Piwowarski, I. Frommholz, M. Lalmas, and K. van Rijsbergen, “What can quantum theory bring to IR?,” in Cikm’10: proceedings of the nineteenth acm conference on conference on information and knowledge management, 2010.
  [Bibtex]

   @inproceedings{Piwowarski2010What-Quantum, Author = {Benjamin Piwowarski and Ingo Frommholz and Mounia Lalmas and Keith van Rijsbergen}, Booktitle = {CIKM'10: Proceedings of the nineteenth ACM conference on Conference on information and knowledge management}, Crossref = {CIKM2010}, Date-Added = {2010-07-19 09:20:38 +0200}, Date-Modified = {2011-04-11 13:31:22 +0100}, Doi = {10.1145/1871437.1871450}, Editor = {Huang, Jimmy and Koudas, Nick and Jones, Gareth and Wu, Xindong and Collins-Thompson, Kevyn and An, Aijun}, Location = {Toronto, Canada}, Publisher = {{ACM}}, Short-Booktitle = {{CIKM}}, Short-Title = {{CIKM}}, Title = {What can Quantum Theory bring to {IR}?}, Type = {International Conference}, Year = {2010}}

• I. Frommholz, B. Larsen, B. Piwowarski, M. Lalmas, P. Ingwersen, and K. van Rijsbergen, “Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework,” in Proceedings of the 3rd iiiX symposium, 2010.
  [Bibtex]

   @inproceedings{Frommholz2010Supporting-Polyrepresentation, Author = {Ingo Frommholz and Birger Larsen and Benjamin Piwowarski and Mounia Lalmas and Peter Ingwersen and Keith van Rijsbergen}, Booktitle = {Proceedings of the 3rd {iiiX} symposium}, Date-Added = {2010-05-30 10:46:21 +0100}, Date-Modified = {2011-04-11 13:31:24 +0100}, Month = {aug}, Title = {Supporting Polyrepresentation in a Quantum-inspired Geometrical Retrieval Framework}, Type = {International Conference}, Year = {2010}}

This paper has been nominated for the best paper award!

[1] B. Piwowarski and M. Lalmas, “Structured information retrieval and quantum theory,” in Proceedings of the third quantum interaction symposium, 2009.
[Bibtex]

 @inproceedings{Piwowarski2009Structured-Information, Abstract = {Information Retrieval (IR) systems try to identify documents relevant to user queries, which are representations of user information needs. Interaction, context, and document structure are three important and active themes in IR research. We present how we propose to model the task of Structured IR (SIR) based on a QT inspired framework, with a focus on how to exploit user contextual information and user interaction in the search process.}, Author = {Benjamin Piwowarski and Mounia Lalmas}, Booktitle = {Proceedings of the Third Quantum Interaction Symposium}, Crossref = {QI2009}, Date-Added = {2009-01-12 12:30:26 +0000}, Date-Modified = {2011-04-11 13:31:22 +0100}, Editor = {Bruza, Peter and Sofge, D. and Lawless, W. and van Rijsbergen, Cornelis J. and Klusch, M.}, Group = {Quantum Information}, Keywords = {quantum}, Location = {Saarbrucken, Germany}, Month = {March}, Publisher = {Springer}, Read = {Yes}, Series = {Lecture Notes in Artificial Intelligence}, Short-Series = {LNCS}, Short-Title = {Proceedings of the 3rd QI Symposium}, Title = {Structured Information Retrieval and Quantum Theory}, Type = {International Workshop}, Volume = {5494}, Year = {2009}}

[2] B. Piwowarski and M. Lalmas, “A Quantum-based Model for Interactive Information Retrieval (extended version),” arXiv, 0906.4026, , 2009.
[Bibtex]

 @techreport{Piwowarski2009A-Quantum-based-Model, Abstract = {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 interac- tion between users and a retrieval system, such as a web search engine, can be exploited to provide better answers to users. Interactive Informa- tion 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. There are many pos- sible interactions between a user and a search system, ranging from query (re)formulation to relevance feedback. However, capturing them within a single framework is difficult and previously proposed approaches have mostly focused on relevance feedback. 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, whose strong geometric component can be a key towards a successful interactive IR model.}, Author = {Benjamin Piwowarski and Mounia Lalmas}, Date-Added = {2009-06-22 17:29:45 +0100}, Date-Modified = {2011-04-11 13:31:21 +0100}, Group = {Quantum Information; Information Retrieval}, Institution = {arXiv}, Month = {September}, Number = {0906.4026}, Title = {{A Quantum-based Model for Interactive Information Retrieval (extended version)}}, Year = {2009}}

 @inproceedings{Piwowarski2010Filtering-documents, Author = {Benjamin Piwowarski and Ingo Frommholz and Yashar Moshfeghi and Mounia Lalmas and Keith van Rijsbergen}, Booktitle = {{Advances in Information Retrieval}}, Crossref = {ECIR2010}, Date-Added = {2009-11-24 11:41:39 +0000}, Date-Modified = {2011-04-11 13:31:24 +0100}, Editor = {Cathal Gurrin and Yulan He and Gabriella Kazai and Udo Kruschwitz and Suzanne Little and Thomas Roelleke and Stefan R{\"u}ger and Keith van Rijsbergen}, Isbn = {978-3-642-12274-3}, Location = {Heidelberg}, Publisher = {Springer}, Series = {Lecture Notes in Computer Science}, Short-Series = {LNCS}, Title = {Filtering documents with subspaces}, Type = {International Conference}, Volume = {5993}, Year = {2010}}