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Vu, Thanh; Willis, Alistair and Song, Dawei
(2015).
DOI: https://doi.org/10.1145/2740908.2742714
URL: http://www.www2015.it/documents/proceedings/compan...
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.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
