modeling the evolution of context in information retrieval

An Information Retrieval (IR) system ranks documents according to their predicted relevance to a formulated query. The prediction depends on the ranking algorithm adopted and on the assumptions about relevance underlying the algorithm. The main assumption is that there is one user, one information need for each query, one location where the user is, and no temporal dimension. But this assumption is unlikely: relevance is context-dependent. Exploiting the context in a way that does not require an high user effort may be effective in IR as suggested for example by Implicit Relevance Feedback techniques. The high number of factors to be considered by these techniques suggests the adoption of a theoretical framework which naturally incorporates multiple sources of evidence. Moreover, the information provided by the context might be a useful source of evidence in order to personalize the results returned to the user. Indeed, the information need arises and evolves in the present and past context of the user. Since the context changes in time, modeling the way in which the context evolves might contribute to achieve personalization. Starting from some recent reconsiderations of the geometry underlying IR and their contribution to modeling context, in this paper some issues which will be the starting point for my PhD research activity are discussed

Web search model based on user context information and collaborative filtering techniques

A pesar del continuo desarrollo que han tenido los buscadores Web modernos, estos aún no satisfacen a cabalidad las necesidades de los usuarios, siendo la relevancia de los documentos recuperados uno de los principales aspectos que afectan la calidad de búsqueda. En este artículo se propone un modelo de meta buscador Web que integra el filtrado colaborativo (basado en ítems) con la propuesta de Massimo Melucci, que se basa en proyectores sobre planos que se originan en la información del contexto del usuario. El modelo fue implementado en un meta buscador Web que recupera documentos de buscadores tradicionales como Google y Bing, donde se muestran los resultados por medio de una lista de documentos ordenados por relevancia, basado en la información del contexto del usuario y en la retroalimentación colaborativa de la comunidad. El modelo propuesto se constituye en un aporte para el área de recuperación de información, dado que muestra promisorios resultados en pruebas realizadas sobre colecciones cerradas y con usuarios.Despite the continuous development modern Web browsers have had, they have not fulfilled user needs, and the retrieved documents relevance is one of the main issues affecting the search quality. The proposed web search meta model engine integrates Web search collaborative filtering (based on items) to Massimo Melucci’s proposal that is based on projectors on plans that came in the user context information. The obtained model was implemented in a meta search site that retrieves documents from traditional search engines like Google and Bing. It presents the results to the user through a list of documents sorted by relevance based on information from the user’s context and the collaborative community feedback. The proposed model constitutes a contribution to the field of information retrieval, since it shows promising results in both closed collections and open collections tests

Web search model based on user context information and collaborative filtering techniques

A pesar del continuo desarrollo que han tenido los buscadores Web modernos, estos aún no satisfacen a cabalidad las necesidades de los usuarios, siendo la relevancia de los documentos recuperados uno de los principales aspectos que afectan la calidad de búsqueda. En este artículo se propone un modelo de meta buscador Web que integra el filtrado colaborativo (basado en ítems) con la propuesta de Massimo Melucci, que se basa en proyectores sobre planos que se originan en la información del contexto del usuario. El modelo fue implementado en un meta buscador Web que recupera documentos de buscadores tradicionales como Google y Bing, donde se muestran los resultados por medio de una lista de documentos ordenados por relevancia, basado en la información del contexto del usuario y en la retroalimentación colaborativa de la comunidad. El modelo propuesto se constituye en un aporte para el área de recuperación de información, dado que muestra promisorios resultados en pruebas realizadas sobre colecciones cerradas y con usuarios.Despite the continuous development modern Web browsers have had, they have not fulfilled user needs, and the retrieved documents relevance is one of the main issues affecting the search quality. The proposed web search meta model engine integrates Web search collaborative filtering (based on items) to Massimo Melucci’s proposal that is based on projectors on plans that came in the user context information. The obtained model was implemented in a meta search site that retrieves documents from traditional search engines like Google and Bing. It presents the results to the user through a list of documents sorted by relevance based on information from the user’s context and the collaborative community feedback. The proposed model constitutes a contribution to the field of information retrieval, since it shows promising results in both closed collections and open collections tests

Lexical measurements for information retrieval: a quantum approach

The problem of determining whether a document is about a loosely defined topic is at the core of text Information Retrieval (IR). An automatic IR system should be able to determine if a document is likely to convey information on a topic. In most cases, it has to do it solely based on measure- ments of the use of terms in the document (lexical measurements). In this work a novel scheme for measuring and representing lexical information from text documents is proposed. This scheme is inspired by the concept of ideal measurement as is described by Quantum Theory (QT). We apply it to Information Retrieval through formal analogies between text processing and physical measurements. The main contribution of this work is the development of a complete mathematical scheme to describe lexical measurements. These measurements encompass current ways of repre- senting text, but also completely new representation schemes for it. For example, this quantum-like representation includes logical features such as non-Boolean behaviour that has been suggested to be a fundamental issue when extracting information from natural language text. This scheme also provides a formal unification of logical, probabilistic and geometric approaches to the IR problem. From the concepts and structures in this scheme of lexical measurement, and using the principle of uncertain conditional, an “Aboutness Witness” is defined as a transformation that can detect docu- ments that are relevant to a query. Mathematical properties of the Aboutness Witness are described in detail and related to other concepts from Information Retrieval. A practical application of this concept is also developed for ad hoc retrieval tasks, and is evaluated with standard collections. Even though the introduction of the model instantiated here does not lead to substantial perfor- mance improvements, it is shown how it can be extended and improved, as well as how it can generate a whole range of radically new models and methodologies. This work opens a number of research possibilities both theoretical and experimental, like new representations for documents in Hilbert spaces or other forms, methodologies for term weighting to be used either within the proposed framework or independently, ways to extend existing methodologies, and a new range of operator-based methods for several tasks in IR

Foundations research in information retrieval inspired by quantum theory

In the information age information is useless unless it can be found and used, search engines in our time thereby form a crucial component of research. For something so crucial, information retrieval (IR), the formal discipline investigating search, can be a confusing area of study. There is an underlying difficulty, with the very definition of information retrieval, and weaknesses in its operational method, which prevent it being called a 'science'. The work in this thesis aims to create a formal definition for search, scientific methods for evaluation and comparison of different search strategies, and methods for dealing with the uncertainty associated with user interactions; so that one has the necessary formal foundation to be able to perceive IR as "search science". The key problems restricting a science of search pertain to the ambiguity in the current way in which search scenarios and concepts are specified. This especially affects evaluation of search systems since according to the traditional retrieval approach, evaluations are not repeatable, and thus not collectively verifiable. This is mainly due to the dependence on the method of user studies currently dominating evaluation methodology. This evaluation problem is related to the problem of not being able to formally define the users in user studies. The problem of defining users relates in turn to one of the main retrieval-specific motivations of the thesis, which can be understood by noticing that uncertainties associated with the interpretation of user interactions are collectively inscribed in a relevance concept, the representation and use of which defines the overall character of a retrieval model. Current research is limited in its understanding of how to best model relevance, a key factor restricting extensive formalization of the IR discipline as a whole. Thus, the problems of defining search systems and search scenarios are the principle issues preventing formal comparisons of systems and scenarios, in turn limiting the strength of experimental evaluation. Alternative models of search are proposed that remove the need for ambiguous relevance concepts and instead by arguing for use of simulation as a normative evaluation strategy for retrieval, some new concepts are introduced that can be employed in judging effectiveness of search systems. Included are techniques for simulating search, techniques for formal user modelling and techniques for generating measures of effectiveness for search models. The problems of evaluation and of defining users are generalized by proposing that they are related to the need for an unified framework for defining arbitrary search concepts, search systems, user models, and evaluation strategies. It is argued that this framework depends on a re-interpretation of the concept of search accommodating the increasingly embedded and implicit nature of search on modern operating systems, internet and networks. The re-interpretation of the concept of search is approached by considering a generalization of the concept of ostensive retrieval producing definitions of search, information need, user and system that (formally) accommodates the perception of search as an abstract process that can be physical and/or computational. The feasibility of both the mathematical formalism and physical conceptualizations of quantum theory (QT) are investigated for the purpose of modelling the this abstract search process as a physical process. Techniques for representing a search process by the Hilbert space formalism in QT are presented from which techniques are proposed for generating measures for effectiveness that combine static information such as term weights, and dynamically changing information such as probabilities of relevance. These techniques are used for deducing methods for modelling information need change. In mapping the 'macro level search' process to 'micro level physics' some generalizations were made to the use and interpretation of basic QT concepts such the wave function description of state and reversible evolution of states corresponding to the first and second postulates of quantum theory respectively. Several ways of expressing relevance (and other retrieval concepts) within the derived framework are proposed arguing that the increase in modelling power by use of QT provides effective ways to characterize this complex concept. Mapping the mathematical formalism of search to that of quantum theory presented insightful perspectives about the nature of search. However, differences between the operational semantics of quantum theory and search restricted the usefulness of the mapping. In trying to resolve these semantic differences, a semi-formal framework was developed that is mid-way between a programmatic language, a state-based language resembling the way QT models states, and a process description language. By using this framework, this thesis attempts to intimately link the theory and practice of information retrieval and the evaluation of the retrieval process. The result is a novel, and useful way for formally discussing, modelling and evaluating search concepts, search systems and search processes

Exploring a Mechanics for Context Aware Information Retrieval

none1- ISSN:noneMELUCCI M.Melucci, Massim