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| Content Provider | ACM Digital Library |
|---|---|
| Author | Vu, Thanh Tien Song, Dawei Willis, Alistair |
| Abstract | Recent research has shown that mining and modelling search tasks helps improve the performance of search personalisation. Some approaches have been proposed to model a search task using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. A limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the previous studies largely ignored the dynamic nature of the search task; with the change of time, the search intent and user interests may also change. This paper addresses these problems by modelling search tasks with time-awareness using latent topics, which are automatically extracted from the task's relevance documents by an unsupervised topic modelling method (i.e., Latent Dirichlet Allocation). In the experiments, we utilise the time-aware search task to re-rank result list returned by a commercial search engine and demonstrate a significant improvement in the ranking quality. |
| Starting Page | 131 |
| Ending Page | 132 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450334730 |
| DOI | 10.1145/2740908.2742714 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-05-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Latent topics Search personalisation Time-aware search task |
| Content Type | Text |
| Resource Type | Article |
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