By by Pontus Johansson.
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Extra resources for Design and development of recommender dialogue systems
G. there might only be one movie in the database that matches the criteria of a class utterance. 2 is an example of this. However, since this is unknown to the client at utterance-time, it is still considered a class utterance. Similarly, a class defined by an utterance may obviously result in zero instances. 1 Information Requests The most common use of information requests as part of the recommendation dialogue is when clients need to verify that they have seen a recommended movie. 1). Clients also frequently ask for information before deciding R1: C1: R2: C2: have you seen The Bone Collector?
McNee et al. conclude that their notion of mixed-initiative does not provide a sensible alternative, since their evaluation shows that users were the least likely to return to the system, and had the worst preference models compared to both purely system-driven and purely user-driven interfaces. The CC, assisted browsing, CCM, and mixed-initiative approaches all use purely graphical user interfaces with direct manipulation and typing of search terms as means of interaction, even though the CC, assisted browsing, and CCM models all acknowledge the possibility to use natural language interaction [16, 13, 62].
User preference models are—as in the case of cf models—long-term and improved as users rate more items in the domain. The advantages and disadvantages are basically the same as cf systems with two important exceptions. On the one hand, cb systems can not identify cross-genre items and thus tend to stick to the same type of recommendations, whereas cf systems can introduce new types (see above). On the other hand, the new-item cold-start problem is not apparent in cb systems, since all its features are known as soon as it is introduced and not dependent on user ratings.