This paper presents a unifying framework to model case-based reasoning recommender
systems (CBR-RSs). CBR-RSs have complex architectures and specialize the CBR problem …

There is increasing awareness in recommender systems research of the need to make the
recommendation process more transparent to users. Following a brief review of existing …

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 …

We describe recommender systems and especially case-based recommender systems. We
define a framework in which these systems can be understood. The framework contrasts …

The demand for ubiquitous information processing over the Web has called for the
development of context-aware recommender systems capable of dealing with the problems …

Critiquing is a powerful style of feedback for case-based recommender systems. Instead of
providing detailed feature values, users indicate a directional preference for a feature. For …

Case-based reasoning systems usually accept the conventional similarity assumption
during retrieval, preferring to retrieve a set of cases that are maximally similar to the target …

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and
unstructured decision-making problems in many different areas. The traditional CBR …

Recommender Systems are the set of tools and techniques to provide useful
recommendations and suggestions to the users to help them in the decision-making process …

Increasingly in case-based reasoning (CBR) approaches to product recommendation, some
or all of the user's requirements are treated, at least initially, as constraints that the retrieved …