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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhao, Qiankun Liu, Tie-Yan Ma, Wei-Ying Hoi, Steven C. H. Bhowmick, Sourav S. Lyu, Michael R. |
| Abstract | It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data. With a set of user-defined calendar schema and calendar patterns, our time-dependent query similarity model is constructed using the marginalized kernel technique, which can exploit both explicit similarity and implicit semantics from the click-through data effectively. Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches. Moreover, we observe that our time-dependent query similarity model can, to some extent, reflect real-world semantics such as real-world events that are happening over time. |
| Starting Page | 543 |
| Ending Page | 552 |
| Page Count | 10 |
| File Format | |
| ISBN | 1595933239 |
| DOI | 10.1145/1135777.1135858 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2006-05-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Event detection Click-through data Marginalized kernel Semantic similarity measure Evolution pattern |
| Content Type | Text |
| Resource Type | Article |
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