Name
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). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2505668 | CIKM |
Keywords | Field | DocType |
low web coverage,web search query log,billion web page,large scale web page,surfing behavior,web page,large-scale commercial web search,billion web page visit,hybrid page,web user,page importance,ranking web search result,ranking | Web design,Static web page,Web search engine,Data mining,World Wide Web,Information retrieval,Web page,Computer science,Web navigation,Backlink,Page view,Web crawler | Conference |
Citations | PageRank | References |
1 | 0.34 | 32 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shatlyk Ashyralyyev | 1 | 1 | 0.34 |
| B. Barla Cambazoglu | 2 | 735 | 38.87 |
| Cevdet Aykanat | 3 | 996 | 84.08 |