Harnessing and Advancing Social Search (HASS): Understanding User Intent, Information Need and Temporal Relevance

• Research unit: COST
• Project number: C11.0132

Key words (English)

Information retrieval; interactive search; social search; understanding user intent; information needs

Research programs (English)

COST-Action IC1002 - Multilingual and multifaceted interactive information access (MUMIA)

Short description (English)

In this project we propose to investigate searching and browsing in social sites and determine where the oneshot 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.

Further information (English)

Full name of research-institution/enterprise: Università della Svizzera italiana Facoltà di Scienze informatiche, USI

Partners and International Organizations (English)

AT; BE; BG; HR; CY; DK; EE; FI; FR; F.Y. R. Macedonia; DE; EL; HU; IE; IL; IT; LT; NL; NO; PL; PT; RO; RS; SI; ES; SE; UK; RU

Abstract (English)

In this project we investigate searching and browsing in social sites (particularly micro blogs) 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. In doing so we gain a more complete understanding of the search behaviour and needs of users on micro blogs and how this differs from more traditional search environments as reported on in existing literature. Based on these insights we 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 and to recommend items of interest to users. The proposed work significantly extends earlier work in personalisation of social media search and latent topic models carried out by the applicants.

References in databases (English)

Swiss Database: COST-DB of the State Secretariat for Education and Research Hallwylstrasse 4 CH-3003 Berne, Switzerland Tel. [phone] Swiss Project-Number: C11.0132