Bayesian extension to the language model for ad hoc information retrieval
Hugo Zaragoza, Djoerd Hiemstra, Mike Tipping
Information Retrieval, Ad Hoc Retrieval, Ad Hoc Language Model, Bayesian Language Model
We propose a Bayesian extension to the ad-hoc Language Model. Many smoothed estimators used for the multinomial query model in ad-hoc Language Models (including Laplace and Bayes-smoothing) are approximations to the Bayesian predictive distribution. In this paper we derive the full predictive distribution in a form amenable to implementation by classical IR models, and then compare it to other currently used estimators. In our experiments the proposed model outperforms Bayes-smoothing, and its combination with linear interpolation smoothing outperforms all other estimators.
Proceedings of the 26th International ACM SIGIR Conference