Usage Meets Link Analysis: Towards Improving Site Specific and Intranet Search via Usage Statistics ∗

In this paper, we explore the possibility of incorporating usage statistics to improve ranking quality in site specific and intranet search engines. We introduce a number of usage based ranking approaches including a PageRank extension, Usage aware PageRank (UPR), an extension to HITS (UHITS), and a naïve approach that uses number of visits to pages as a quality measure. We compare these methods against each other and against two major link analysis approaches (PageRank and HITS). We investigate weighting schemes that take into account the probability of visiting a page directly (by typing or via bookmarks), as well as the relative probability of following a particular link from a given page. Both of these probabilities can be approximated from usage logs. We developed a site specific search engin

Expert agreement and content based reranking in a meta search environment using Mearf

Recent increase in the number of search engines on the Web and the availability of meta search engines that can query multiple search engines makes it important to find effective methods for combining results coming from different sources. In this paper we introduce novel methods for reranking in a meta search environment based on expert agreement and contents of the snippets. We also introduce an objective way of evaluating different methods for ranking search results that is based upon implicit user judgements. We incorporated our methods and two variations of commonly used merging methods in our meta search engine, Mearf, and carried out an experimental study using logs accumulated over a period of twelve months. Our experiments show that the choice of the method used for merging the output produced by different search engines plays a significant role in the overall quality of the search results. In almost all cases examined, results produced by some of the new methods introduced were consistently better than the ones produced by traditional methods commonly used in various meta search engines. These observations suggest that the proposed methods can offer a relatively inexpensive way of improving the meta search experience over existing methods

Usage Aware PageRank

Traditional link analysis approaches assume equal weights assigned to different links and pages. In original PageRank formulation, the user model assumes that the user has equal probability to follow each link from a given page, thus the score of a page equally affects all of the pages it points to. It also assumes that the probability for a user to go to a URL directly without following a link is the same for all URLs. In this paper, we investigate different weighting schemes that take into account the probability to go to a page directly (by typing or using bookmarks), as well as the relative probability to follow a link from a given page. Both of these probabilities can be approximated from usage logs if they are available. We introduce a naturalextension to the original PageRank formulation that we will call Usage aware PageRank (UPR). The new formulation combines static link structure graph with the usage graph that will be obtained via web logs or other means. It is also quite general; how much emphasis will be given to the graphs is controlled by a parameter. If the parameter is set to zero, the algorithm becomes equivalent to the original PageRank, if it is set to one, the emphasis shifts to the usage graph, and for values in between, both of the graphs will be used with weights specified by the parameter. UPR is also quite inexpensive. After a onetime precalculation step, an iteration of UPR takes about the same time as a PageRank iteration