Query expansion of pseudo relevance feedback based on matrix-weighted association rules mining

An algorithm of matrix-weighted association rule mining for query expansion is presented based on the quadruple pruning, and a related theorem and its proof are given. This method can tremendously enhance the mining efficiency. Experimental results demonstrate that its mining time is averagely reduced by 87.84%, compared to that of the original one. And a query expansion algorithm of pseudo relevance feedback is proposed based on matrix-weighted association rule mining, which combines the association rules mining technique with the query expansion. The algorithm can automatically mine those matrix-weighted association rules related to the original query in the top-ranked retrieved documents to construct an association rules-based database, and extract expansion terms related to the original query from the database for query expansion. At the same time, a new computing method for weights of expansion terms is given. It makes the weighted value of an expansion term more reasonable. Experimental results show that this method is better than traditional ones in average precision.