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| Content Provider | ACM Digital Library |
|---|---|
| Author | Mishne, Gilad Zhu, Guangyu |
| Copyright Year | 2012 |
| Abstract | User browsing information, particularly non-search-related activity, reveals important contextual information on the preferences and intents of Web users. In this article, we demonstrate the importance of mining general Web user behavior data to improve ranking and other Web-search experience, with an emphasis on analyzing individual user sessions for creating aggregate models. In this context, we introduce $\textit{ClickRank},$ an efficient, scalable algorithm for estimating Webpage and Website importance from general Web user-behavior data. We lay out the theoretical foundation of ClickRank based on an intentional surfer model and discuss its properties. We quantitatively evaluate its effectiveness regarding the problem of Web-search ranking, showing that it contributes significantly to retrieval performance as a novel Web-search feature. We demonstrate that the results produced by ClickRank for Web-search ranking are highly competitive with those produced by other approaches, yet achieved at better scalability and substantially lower computational costs. Finally, we discuss novel applications of ClickRank in providing enriched user Web-search experience, highlighting the usefulness of our approach for nonranking tasks. |
| Starting Page | 1 |
| Ending Page | 22 |
| Page Count | 22 |
| File Format | |
| ISSN | 15591131 |
| e-ISSN | 1559114X |
| DOI | 10.1145/2109205.2109206 |
| Volume Number | 6 |
| Issue Number | 1 |
| Journal | ACM Transactions on the Web (TWEB) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-03-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | ClickRank Web search Aggregate user behavior Intentional surfer model Learning to rank |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications |
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