EXPLANATION IN RECOMMENDER SYSTEMS 183 3. Incremental Nearest Neighbour In this section, a brief overview of conversational CBR(Aha et al 2001)in the context of product recommendation is followed by a detailed account of the recommendation process in iNn and the important role played by the concept of case dominance in the approach. One distinguishing feature of our approach is a goal-driven attribute selection strategy that has been shown to be very effective in reducing the length of recommendation dialogues(McSherry, 2003a) Another is a simple mechanism for ensuring that the dialogue is ter- minated only when it is certain that a more similar case will not be found if the dialogue is allowed to continue 3. 1. Conversational CBr In 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 preferences of the user. In con- versational CBR(CCBR)approaches like INN, a query is incremer tally (and often incompletely) elicited in an interactive dialogue with the user. We focus here on approaches in which the retrieval of recom- mended cases is based on their similarity to the elicited query, rather than relying on exact matching as in most decision-tree approaches (e.g. Doyle and Cunningham, 2000: McSherry, 2001b) Given a query Q over a subset Ao of the case attributes A, the similarity of any case C to Q is typically defined to be sim(C, 2)=> wa sima(C, 2), where for each a E A, Wa is the importance weight assigned to a and sima(C, @)is a local measure of the similarity of a(C), the value of a in C, to Ia(Q), the preferred value of a. As usual in practice, we assume that0≤sima(x,y)≤ I for all a∈ a and that sim(x,y)=lif nd only if x=y. We also assume that for each a E A, the distance measure I-sima satisfies the triangle inequalit a generic algorithm for CCBR in product recommendation (CCBR- PR)is shown in Figure 1. At each stage of the recommenda- tion dialogue, the system selects the next most useful attribute, asks the user for the preferred value, and retrieves the case (or product)EXPLANATION IN RECOMMENDER SYSTEMS 183 3. Incremental Nearest Neighbour In this section, a brief overview of conversational CBR (Aha et al., 2001) in the context of product recommendation is followed by a detailed account of the recommendation process in iNN and the important role played by the concept of case dominance in the approach. One distinguishing feature of our approach is a goal-driven attribute selection strategy that has been shown to be very effective in reducing the length of recommendation dialogues (McSherry, 2003a). Another is a simple mechanism for ensuring that the dialogue is ter￾minated only when it is certain that a more similar case will not be found if the dialogue is allowed to continue. 3.1. Conversational CBR In 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 preferences of the user. In con￾versational CBR (CCBR) approaches like iNN, a query is incremen￾tally (and often incompletely) elicited in an interactive dialogue with the user. We focus here on approaches in which the retrieval of recom￾mended cases is based on their similarity to the elicited query, rather than relying on exact matching as in most decision-tree approaches (e.g. Doyle and Cunningham, 2000; McSherry, 2001b). Given a query Q over a subset AQ of the case attributes A, the similarity of any case C to Q is typically defined to be: sim(C, Q)=
a∈AQ wa sima(C, Q), where for each a ∈ A,wa is the importance weight assigned to a and sima(C, Q) is a local measure of the similarity of πa(C), the value of a in C, to πa(Q), the preferred value of a. As usual in practice, we assume that 0 ≤ sima(x, y) ≤ 1 for all a ∈ A and that sima(x, y) = 1 if and only if x = y. We also assume that for each a ∈ A, the distance measure 1−sima satisfies the triangle inequality. A generic algorithm for CCBR in product recommendation (CCBR-PR) is shown in Figure 1. At each stage of the recommenda￾tion dialogue, the system selects the next most useful attribute, asks the user for the preferred value, and retrieves the case (or product)