180 D MCSHERRY argue that extracting meaningful explanations from the computational models on which recommendations are based is a challenge that must e addressed to enable the development of recommender systems that are more understandable, more effective, and more acceptable. It is an argument that seems equally compelling in collaborative and content based approaches to product recommendation McSherry (2003a)proposes a case-base reasoning(CBR)approach to product recommendation that combines an effective strategy for reducing the length of recommendation dialogues with a mechanism for ensuring that the dialogue is terminated only when it is certain that the recommendation will be the same no matter how the user chooses to extend her query. Referring to the approach as incremen- tal nearest neighbour (iNN), we focus here on the benefits it offers terms of making the recommendation process more transparent to users. One advantage is that recommendations based on incomplete queries can be justified on the grounds that the user's preferences with respect to attributes not mentioned in her query cannot affect the out come. We also show how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recom ommender systems and some of the pproaches to explanation in rec- In Section 2. we examine existing essons learned from this research In Section 3, we present a detailed account of the recommendation process in inN and the important role played by the concept of case dominance in the approach. In Section 4, we present an approach to explanation in which there is no requirement for domain knowledge other than the similarity knowledge and cases already available to the system. We demonstrate the approach in a mixed-initiative recom- mender system called Top Case which can explain the relevance of any question the user is asked in strategic terms, recognise when the dia- logue can be safely terminated, and justify its recommendations on the grounds that any un-elicited preferences of the user cannot affect the outcome. Related work is discussed in Section 5 and our conclusions are presented in Section 6 2. Existing Approaches Herlocker et al.(2000)evaluated several approaches to explanation in the collaborative movie recommender movie Lens in terms of their180 D. MCSHERRY argue that extracting meaningful explanations from the computational models on which recommendations are based is a challenge that must be addressed to enable the development of recommender systems that are more understandable, more effective, and more acceptable. It is an argument that seems equally compelling in collaborative and content￾based approaches to product recommendation. McSherry (2003a) proposes a case-base reasoning (CBR) approach to product recommendation that combines an effective strategy for reducing the length of recommendation dialogues with a mechanism for ensuring that the dialogue is terminated only when it is certain that the recommendation will be the same no matter how the user chooses to extend her query. Referring to the approach as incremen￾tal nearest neighbour (iNN), we focus here on the benefits it offers in terms of making the recommendation process more transparent to users. One advantage is that recommendations based on incomplete queries can be justified on the grounds that the user’s preferences with respect to attributes not mentioned in her query cannot affect the out￾come. We also show how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recom￾mendation process. In Section 2, we examine existing approaches to explanation in rec￾ommender systems and some of the lessons learned from this research. In Section 3, we present a detailed account of the recommendation process in iNN and the important role played by the concept of case dominance in the approach. In Section 4, we present an approach to explanation in which there is no requirement for domain knowledge other than the similarity knowledge and cases already available to the system. We demonstrate the approach in a mixed-initiative recom￾mender system called Top Case which can explain the relevance of any question the user is asked in strategic terms, recognise when the dia￾logue can be safely terminated, and justify its recommendations on the grounds that any un-elicited preferences of the user cannot affect the outcome. Related work is discussed in Section 5 and our conclusions are presented in Section 6. 2. Existing Approaches Herlocker et al. (2000) evaluated several approaches to explanation in the collaborative movie recommender MovieLens in terms of their