document: A generalized Co-HITS algorithm and its application to bipartite graphs Recently many data types arising from data mining and Web search applications can be modeled as bipartite graphs . Examples include queries and URLs in query logs , and authors and papers in scientific literature . However , one of the issues is that previous algorithms only consider the content and link information from one side of the bipartite graph . There is a lack of constraints to make sure the final relevance of the score propagation on the graph , as there are many noisy edges within the bipartite graph . In this paper , we propose a novel and general Co-HITS algorithm to incorporate the bipartite graph with the content information from both sides as well as the constraints of relevance . Moreover , we investigate the algorithm based on two frameworks , including the iterative and the regularization frameworks , and illustrate the generalized Co-HITS algorithm from different views . For the iterative framework , it contains HITS and personalized PageRank as special cases . In the regularization framework , we successfully build a connection with HITS , and develop a new cost function to consider the direct relationship between two entity sets , which leads to a significant improvement over the baseline method . To illustrate our methodology , we apply the Co-HITS algorithm , with many different settings , to the application of query suggestion by mining the AOL query log data . Experimental results demonstrate that CoRegu-0 .5 ( i.e. , a model of the regularization framework ) achieves the best performance with consistent and promising improvements .
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