HASS - Harnessing and Advancing Social Search (HASS): Understanding User Intent, Information Need and Temporal Relevance
We propose to investigate searching and browsing in social sites and determine where the one-shot and one-size-fits-all paradigm of search is failing users and does not sufficiently assist them with their information gathering task. We will use modern statistical learning techniques to develop models that are able to utilise personalisation, temporal task-based knowledge and topical information derived from the corpus to improve search. The proposed work will significantly extend earlier work in personalisation of social media search and latent topic models carried out by the applicant. These new models will better serve users their information needs and better support them in completing more complex tasks over multiple queries or even sessions. Furthermore the models will provide better insight into the data contained in social sites including information about the topics represented and how their use and popularity is varying over time.