Probabilistic Query Expansion Method Based on a Query Recommendation Algorithm
Btihal El Ghali, Abderrahim El Qadi, Omar El Midaoui, Mohamed Ouadou, Driss Aboutajdine
International Journal of Web Applications, Vol. 5, No. 1, 2013.
DOI: 
Link: http://www.dline.info/ijwa/fulltext/v5n1/1.pdf
Abstract:
Query Expansion Methods are proposed to solve many problems of information retrieval systems, but most of these methods do not use the information of interactions between the users and the system. In our approach, we applied a Query Recommendation Algorithm on a list of past user queries, to extract the most associated queries to the input query, and used it in a Probabilistic Query Expansion method, that is constructed as a language model to search in set of candidate terms for the most relevant terms for the initial query that we have to expand. The output of this approach is a table of terms that are candidates for the expansion of the user query, and their values of correlation with the whole query. We did our experiments using the database CISI of the standard collection of test SMART. The results show that the best values are reached by adding fifteen terms to the input queries, using the five most relevant documents of the input query and the five most relevant documents for each of the recommended queries used, for short and long queries. Our experiments shows also, that for short queries we need to use just the best recommended query in the process of expansion to have a very high value of the Interpolated Average Precision (IAP), but concerning long queries we need to employ also the second best recommended query to have the highest value of IAP. The final experiment was a comparison between our approach and the Rocchio Algorithm, and it shows that our approach gives best results except in the case of using four Recommended Queries (RQs) for short input queries.