Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning

Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service the advisor as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.