We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR) application. We show the results of our implementations for several sparse matrix algorithms such as Coordinate Storage (COO), Compressed Sparse Column (CSC), Compressed Sparse Row (CSR), and Block Sparse Row (BSR) sparse matrix algorithms, using a standard text collection. Evaluation is based on the storage space requirement for each indexing structure and the efficiency of the query-processing algorithm. Our results demonstrate that CSR is more efficient in terms of storage space requirement and query processing timing over the other sparse matrix algorithms for Information Retrieval application. Furthermore, we experimentally evaluate the mapping of various existing index compression techniques used to compress index in information retrieval systems (IR) on Compressed Sparse Row Information Retrieval (CSR IR).