Article,
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review, 24 (3-4): 319--338 (2005)
DOI: http://dx.doi.org/10.1007/s10462-005-9000-z
Abstract
In case-based reasoning (CBR) approaches to product recommendation, descriptions of the available products are stored in a case library and retrieved in response to a query representing the user's requirements. We present an approach to recovery from the <Emphasis Type="Italic">retrieval failures that often occur when the user's requirements are treated as constraints that must be satisfied. Failure to retrieve a matching case triggers a recovery process in which the user is invited to select from a <Emphasis Type="Italic">recovery <Emphasis Type="Italic">set of relaxations (or sub-queries) of her query that are guaranteed to succeed. The suggested relaxations are ranked according to a simple measure of <Emphasis Type="Italic">recovery cost defined in terms of the importance weights assigned to the query attributes. The recovery set for an unsuccessful query also serves as a guide to continued exploration of the product space when none of the cases initially recommended by the system is acceptable to the user
