Caracterización Formal y Análisis Empírico de Mecanismos Incrementales de Búsqueda basados en Contexto
This work addresses the vocabulary gap problem in web search for users, though it appears incremental as it builds on existing information retrieval techniques.
The authors tackled the problem of information retrieval systems not utilizing users' contextual information by proposing a semisupervised technique that learns novel terms to bridge the vocabulary gap between user knowledge and relevant web documents. Experimental results showed significant improvements over existing techniques, with the method evolving high-quality queries that retrieve context-relevant results.
The Web has become a potentially infinite information resource, turning into an essential tool for many daily activities. This resulted in an increase in the amount of information available in users' contexts that is not taken into account by current information retrieval systems. This thesis proposes a semisupervised information retrieval technique that helps users to recover context relevant information. The objective of the proposed technique is to reduce the vocabulary gap existing between the knowledge a user has about a specific topic and the relevant documents available in the Web. This thesis presents a method for learning novel terms associated with a thematic context. This is achieved by identifying those terms that are good descriptors and good discriminators of the user's current thematic context. In order to evaluate the proposed method, a theoretical framework for the evaluation of search mechanisms was developed. This served as a guide for the implementation of an evaluation framework that allowed to compare the techniques proposed in this thesis with other techniques existing in the literature. The experimental evidence indicates that the methods proposed in this thesis present significant improvements over previously published techniques. In addition, the evaluation framework was equipped with novel evaluation metrics that favor the exploration of novel material and incorporates a semantic relationship metric between documents. The algorithms developed in this thesis evolve high quality queries, which have the capability of retrieving results that are relevant to the user context. These results have a positive impact on the way users interact with available resources.