asymmetrical query recommendation method based on bipartite network resource allocation
This paper presents a new query recommendation method that generates recommended query list by mining large-scale user logs. Starting from the user logs of click-through data, we construct a bipartite network where the nodes on one side correspond to unique queries, on the other side to unique URLs. Inspired by the bipartite network based resource allocation method, we try to extract the hidden information from the Query-URL bipartite network. The recommended queries generated by the method are asymmetrical which means two related queries may have different strength to recommend each other. To evaluate the method, we use one week user logs from Chinese search engine Sogou. The method is not only `content ignorant', but also can be easily implemented in a paralleled manner, which is feasible for commercial search engines to handle large scale user logs.
asymmetrical query recommendation network resource allocation bipartite network. user log analysis