D MCSHERRY algorithm CCBR-PR Q←Qu{a=w retrieve the case C that is most similar to o until termination criteria are satisfied Figure 1. Conversational CBR in product recommendation. that is most similar to the query that has been elicited so far. The dialogue continues until some predefined termination criteria are satis fied. or until no further attributes remain. The case recommended on each cycle is usually the one that is most similar to the current query r, It is not un similar to a given query, in which case we assume that all such cases are equally recommended. That is, we define the recommendation for en query r(Q={C:sim(C,Q)≥sim(C°,Q) for all c Cases other than those that are maximally similar to the current query may also be presented as alternatives that the user may wish to con- sider, though the number of cases that can be presented to the user may be limited by the available screen space. Of course, cognitive load is another important consideration The defining components of a CCBR-PR algorithm are the strat egy used to select the most useful attribute on each recommendation cycle and the criteria used to decide when the dialogue should be terminated. Possible approaches to attribute selection include giving priority to the most important of the remaining attribut McSherry, 2003a)and the similarity-based approach proposed by Kohlmaier et al.(2001). Various approaches to termination of the rec ommendation dialogue are also possible. For example, the dialogue could be terminated when the current query Q is such that [r(Q)I I or when the similarity of any case reaches a predefined threshold As we shall see in Section 3. 4. the criteria for termination of the184 D. MCSHERRY Figure 1. Conversational CBR in product recommendation. that is most similar to the query that has been elicited so far. The dialogue continues until some predefined termination criteria are satis- fied, or until no further attributes remain. The case recommended on each cycle is usually the one that is most similar to the current query. However, it is not unusual for more than one case to be maximally similar to a given query, in which case we assume that all such cases are equally recommended. That is, we define the recommendation for a given query Q to be: r(Q)= {C : sim(C, Q)≥sim(C◦ , Q) for all C◦ }. Cases other than those that are maximally similar to the current query may also be presented as alternatives that the user may wish to con￾sider, though the number of cases that can be presented to the user may be limited by the available screen space. Of course, cognitive load is another important consideration. The defining components of a CCBR-PR algorithm are the strat￾egy used to select the most useful attribute on each recommendation cycle and the criteria used to decide when the dialogue should be terminated. Possible approaches to attribute selection include giving priority to the most important of the remaining attributes (McSherry, 2003a) and the similarity-based approach proposed by Kohlmaier et al. (2001). Various approaches to termination of the rec￾ommendation dialogue are also possible. For example, the dialogue could be terminated when the current query Q is such that |r(Q)| = 1 or when the similarity of any case reaches a predefined threshold. As we shall see in Section 3.4, the criteria for termination of the