text: 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. There are many possible 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.
  @cite_19: Information retrieval (IR) models based on vector spaces have been investigated for a long time. Nevertheless, they have recently attracted much research interest. In parallel, context has been rediscovered as a crucial issue in information retrieval. This article presents a principled approach to modeling context and its role in ranking information objects using vector spaces. First, the article outlines how a basis of a vector space naturally represents context, both its properties and factors. Second, a ranking function computes the probability of context in the objects represented in a vector space, namely, the probability that a contextual factor has affected the preparation of an object.
summary: The most related work in that field is that of Melucci's @cite_19 , which computes the probability of having a given context @math , where @math is the probability distribution generated by the document vector @math , and the subspace @math is equalled to the context and is built through user interaction. More specifically, given a set of documents deemed relevant, either using user feedback or pseudo-relevance feedback, one can compute a subspace @math corresponding to the principal components of a subspace spanned by those documents. A document vector fully included in this space will be fully relevant (probability of 1), an orthogonal one will be fully irrelevant (zero probability). Melucci's approach is dual to ours, in the sense that instead of representing users in an IN space, he considers documents in a contextual space. Our approach, which relies on an IN space, facilitates the possibility of using the different quantum evolution mechanisms to model the interaction between the user and the retrieval system.