%0 Conference Paper %1 Ashyralyyev:2013:ISB:2505515.2505668 %A Ashyralyyev, Shatlyk %A Cambazoglu, B. Barla %A Aykanat, Cevdet %B Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management %C New York, NY, USA %D 2013 %I ACM %K social-search %P 2351--2356 %R 10.1145/2505515.2505668 %T Incorporating the Surfing Behavior of Web Users into Pagerank %U http://doi.acm.org/10.1145/2505515.2505668 %X In 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) %@ 978-1-4503-2263-8

@inproceedings{Ashyralyyev:2013:ISB:2505515.2505668, abstract = {In 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)}, acmid = {2505668}, added-at = {2017-10-16T02:56:26.000+0200}, address = {New York, NY, USA}, author = {Ashyralyyev, Shatlyk and Cambazoglu, B. Barla and Aykanat, Cevdet}, biburl = {https://www.bibsonomy.org/bibtex/227825c48d69ae25015e713fc307c1aac/brusilovsky}, booktitle = {Proceedings of the 22Nd ACM International Conference on Information \& Knowledge Management}, description = {Incorporating the surfing behavior of web users into pagerank}, doi = {10.1145/2505515.2505668}, interhash = {e07026c42cf5c557a3f6e3f3a1b004e7}, intrahash = {27825c48d69ae25015e713fc307c1aac}, isbn = {978-1-4503-2263-8}, keywords = {social-search}, location = {San Francisco, California, USA}, numpages = {6}, pages = {2351--2356}, publisher = {ACM}, series = {CIKM '13}, timestamp = {2017-10-16T03:02:08.000+0200}, title = {Incorporating the Surfing Behavior of Web Users into Pagerank}, url = {http://doi.acm.org/10.1145/2505515.2505668}, year = 2013 }