Browsing by Subject "Online searching"
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Item Open Access Algorithms for within-cluster searches using inverted files(Springer, 2006-11) Altıngövde, İsmail Şengör; Can, Fazlı; Ulusoy, ÖzgürInformation retrieval over clustered document collections has two successive stages: first identifying the best-clusters and then the best-documents in these clusters that are most similar to the user query. In this paper, we assume that an inverted file over the entire document collection is used for the latter stage. We propose and evaluate algorithms for within-cluster searches, i.e., to integrate the best-clusters with the best-documents to obtain the final output including the highest ranked documents only from the best-clusters. Our experiments on a TREC collection including 210,158 documents with several query sets show that an appropriately selected integration algorithm based on the query length and system resources can significantly improve the query evaluation efficiency. © Springer-Verlag Berlin Heidelberg 2006.Item Open Access Characterizing web search queries that match very few or no results(ACM, 2012-11) Altıngövde, İ. Ş.; Blanco, R.; Cambazoğlu, B. B.; Özcan, Rıfat; Sarıgil, Erdem; Ulusoy, ÖzgürDespite the continuous efforts to improve the web search quality, a non-negligible fraction of user queries end up with very few or even no matching results in leading web search engines. In this work, we provide a detailed characterization of such queries based on an analysis of a real-life query log. Our experimental setup allows us to characterize the queries with few/no results and compare the mechanisms employed by the major search engines in handling them.Item Open Access Incorporating the surfing behavior of web users into PageRank(ACM, 2013-10-11) Ashyralyyev, Shatlyk; Cambazoğlu, B. B.; Aykanat, CevdetIn large-scale commercial web search engines, estimating the importance of a web page is a crucial ingredient in ranking web search results. So far, to assess the importance of web pages, two different types of feedback have been taken into account, independent of each other: the feedback obtained from the hyperlink structure among the web pages (e.g., PageRank) or the web browsing patterns of users (e.g., BrowseRank). Unfortunately, both types of feedback have certain drawbacks. While the former lacks the user preferences and is vulnerable to malicious intent, the latter suffers from sparsity and hence low web coverage. In this work, we combine these two types of feedback under a hybrid page ranking model in order to alleviate the above-mentioned drawbacks. Our empirical results indicate that the proposed model leads to better estimation of page importance according to an evaluation metric that relies on user click feedback obtained from web search query logs. We conduct all of our experiments in a realistic setting, using a very large scale web page collection (around 6.5 billion web pages) and web browsing data (around two billion web page visits). Copyright is held by the owner/author(s).