Probabilistic model for contextual retrieval

ABSTRACT

Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Human Factors, Performance, Theory

Keywords:
contextual retrieval, probabilistic model, query expansion, query log