Towards the end of Chapter 4 we observed how the typical ACF approach is similar to case completion. As we described in Chapter 3, case completion involves problem solving where the goal is to specify the series of steps in a process that will lead to a solution. Thus the solution part of the case is not differentiated from the problem description part of the case. The solution is derived by gradually elaborating the initially under-specified target case. The case description is usually incrementally built up using a dialog with the user.
At each step of the case completion process, cases are retrieved and case completion information suitable for the target case is extracted and offered to the user. The user selects the pieces of completion information he/she deems suitable for the current process, and the cycle resumes until the case has been fully specified (see Figure 6.11a). In some case completion tasks, the order in which the case is completed is not important. For instance, the Nodal system (Cunningham et al. 1998) uses an information theoretic analysis to choose the next feature value to offer the user. However, increasingly dialogue ordering is being recognised as an important part of conversational systems (Aha et al. 1998, Schmitt & Bergman 2001, Bridge 2002). This research recognises that users of dialogue-driven systems expect a sensible ordering to the questions they are required to answer. Also, if the case completion technique describes a process or a plan then there must be constraints to the case completion features available at each step which reflect dependencies between subsections of the task or plan (Bergmann et al. 1998). Therefore, in case completion scenarios we can make a distinction between systems that use surface similarity such as Nodal, and systems that involve structural similarity. Surface similarity as defined by Gentner (1983) concerns feature value similarity, while structural similarity is defined using the relations between features, such as ordering constraints.
We can usefully compare the ACF process to case completion. The comparison will highlight the similarities ACF has with lazy, similarity techniques such as case completion CBR, but also highlight the deficiencies. Using this analysis we will be able to draw upon techniques used in case completion and apply them to the ACF context.
We can see that the ACF process and the case completion process both involve the incremental elaboration of the target case based on feedback given by the user. An ACF system uses the information it has to hand to retrieve similar user profiles and extract completion information for the case profile which is then offered to the user (see Figure 6.11b).
(a) CBR Case Completion Cycle (b) ACF Recommendation Cycle
Figure 6.11: A comparison of the CBR case completion process with the ACF recommendation cycle
Negative user feedback may move the user toward a different set of neighbour profiles which is then used to make the next set of recommendations. So both techniques are concerned with the stepwise filling out of the target case.
Many CBR case representations are not concerned with structural similarity, and use surface similarity as their only means of assessing similarity. An ACF profile might be considered such a case representation – it is composed of a number of unordered, feature value pairs each representing an item and the rating assigned by the user. Many case completion processes, however, do have some ordering relations between features since tasks cannot be completed in an ad-hoc way. While the data informing an ACF profile does have an ordering relation (it represents a shallow trace of the user’s ‘consumption’ over time), this relation is not represented in the ACF profile. Since the ACF algorithm uses surface similarity only to assess similarity, any semantics associated with the original ordering are discarded. Thus, unlike case completion entities, ACF case completion entities (ACF recommendations) are presented in an ad-hoc way, without concern for the order of the entities preceding them.
So at a surface level we can recognise the resemblance of an ACF profile to a CBR case, but at a structural level we should note that the ordering information in an ACF profile is absent. This observation leads us to consider how we might make use of ordering information in ACF. CBR systems that have temporal or ordering constraints, such as process planning, require sources of domain knowledge that specify the dependencies between process steps. Bergmann et al. (1998) specify three possible sources – a domain-specific reasoning system, user constraints, and a static
analysis of the domain theory. However, all three techniques presuppose a domain model which is entirely absent from ACF applications. In fact, in most ACF recommenders we have no knowledge of the user, and very little knowledge, if any, of the content being used. It is this shallowness of the knowledge available which makes using ordering relations in ACF profiles unhelpful. In Chapter 6 we used a technique called context-boosted ACF to address this lack of ordering by ranking ACF recommendations according to a simple profile based on the most recent ordering relations in the user profile.