Performance Evaluation of State-of-the-Art Ranked Retrieval Methods and Their Combinations for Query Suggestion

This paper investigates several state-of-the-art ranked retrieval methods, adapts and combines them as well for query suggestion. Four performance criteria plus user evaluation have been adopted to evaluate these query suggestion methods in terms of ranking and relevance from different perspectives. Extensive experiments have been conducted using carefully designed eighty test queries which are related to eight topics. The experimental results show that the method developed in this paper, which combines the TF-IDF and Jaccard coefficient methods, is the best method for query suggestion among the six methods evaluated, outperforming the most popularly used TF-IDF method. Furthermore, it is shown that re-ranking query suggestions using Cosine similarity improves the performance of query suggestions.