Semantic Query Suggestion Based on Optimized Random Forests

Abstract

Query suggestion is an integral part of Web search engines. Data-driven approaches to query suggestion aim to identify more relevant queries to users based on term frequencies and hence cannot fully reveal the underlying semantic intent of queries. Semantic query suggestion seeks to identify relevant queries by taking semantic concepts contained in user queries into account. In this paper, we propose a machine learning approach to semantic query suggestion based on Random Forests. The presented scheme employs an optimized Random Forest algorithm based on multi-objective simulated annealing and weighted voting. In this scheme, multi-objective simulated annealing is utilized to tune the parameters of Random Forests algorithm, i.e. the number of trees forming the ensemble and the number of features to split at each node. In addition, the weighted voting is utilized to combine the predictions of trees based on their predictive performance. The predictive performance of the proposed scheme is compared to conventional classification algorithms (such as Naive Bayes, logistic regression, support vector machines, Random Forest) and ensemble learning methods (such as AdaBoost, Bagging and Random Subspace). The experimental results on semantic query suggestion prove the superiority of the proposed scheme.