id: 05342544
dt: a
an: 05342544
au: Salamó, Maria; Reilly, James; McGinty, Lorraine; Smyth, Barry
ti: Knowledge discovery from user preferences in conversational recommendation.
so: Jorge, Alípio M. (ed.) et al., Knowledge discovery in databases: PKDD
    2005. 9th European conference on principles and practice of knowledge
    discovery in databases, Porto, Portugal, October 3‒7, 2005.
    Proceedings. Berlin: Springer (ISBN 978-3-540-29244-9/pbk). Lecture
    Notes in Computer Science 3721. Lecture Notes in Artificial
    Intelligence, 228-239 (2005).
py: 2005
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/11564126_25
ab: Summary: Knowledge discovery for personalizing the product recommendation
    task is a major focus of research in the area of conversational
    recommender systems to increase efficiency and effectiveness.
    Conversational recommender systems guide users through a product space,
    alternatively making product suggestions and eliciting user feedback.
    Critiquing is a common and powerful form of feedback, where a user can
    express her feature preferences by applying a series of directional
    critiques over recommendations, instead of providing specific value
    preferences. For example, a user might ask for a ‘less expensive’
    vacation in a travel recommender; thus ‘less expensive’ is a
    critique over the price feature. The expectation is that on each cycle,
    the system discovers more about the user’s soft product preferences
    from minimal information input. In this paper we describe three
    different strategies for knowledge discovery from user preferences that
    improve recommendation efficiency in a conversational system using
    critiquing. Moreover, we will demonstrate that while the strategies
    work well separately, their combined effort has the potential to
    considerably increase recommendation efficiency even further.
rv: