Various retrieval models have been developed and analyzed so far, but less research aims to an integration of the different models within a common framework. This paper introduces ...
Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its impor...
We discuss a retrieval model in which the task is to complete a sentence, given an initial fragment, and given an application specific document collection. This model is motivate...
In this paper, we propose an information retrieval model called Latent Interest Semantic Map (LISM), which features retrieval composed of both Collaborative Filtering(CF) and Prob...
Search engines process queries conjunctively to restrict the size of the answer set. Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web p...
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
Existing retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model’s empirica...
In this paper, the participants of the panel at the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval answer questions about what multimedia is, how MIR is ...
In this work, we study similarity measures for text-centric XML documents based on an extended vector space model, which considers both document content and structure. Experimenta...
A common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) matching o...