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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fang, Yi Bawab, Ziad Al Crespo, Jean-Francois |
| Copyright Year | 2014 |
| Abstract | Localized query prediction (LQP) is the task of estimating web query trends for a specific location. This problem subsumes many interesting personalized web applications such as personalization for buzz query detection, for query expansion, and for query recommendation. These personalized applications can greatly enhance user interaction with web search engines by providing more customized information discovered from user input (i.e., queries), but the LQP task has rarely been investigated in the literature. Although exist abundant work on estimating $\textit{global}$ web search trends does exist, it often encounters the big challenge of data sparsity when personalization comes into play. In this article, we tackle the LQP task by proposing a series of collaborative language models (CLMs). CLMs alleviate the data sparsity issue by collaboratively collecting queries and trend information from the other locations. The traditional statistical language models assume a fixed background language model, which loses the taste of personalization. In contrast, CLMs are personalized language models with flexible background language models customized to various locations. The most sophisticated CLM enables the collaboration to adapt to specific query topics, which further advances the personalization level. An extensive set of experiments have been conducted on a large-scale web query log to demonstrate the effectiveness of the proposed models. |
| Starting Page | 1 |
| Ending Page | 21 |
| Page Count | 21 |
| File Format | |
| ISSN | 21606455 |
| e-ISSN | 21606463 |
| DOI | 10.1145/2622617 |
| Volume Number | 4 |
| Issue Number | 2 |
| Journal | ACM Transactions on Interactive Intelligent Systems (TiiS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-06-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Query log mining Discriminative models Generative models Language models Trending topic recommendation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Human-Computer Interaction |
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