L Chen P Pu over any combination of features with freedom. In fact, the purpose of this type of critiquing support is to assist users in freely executing tradeoff navigation, which a process shown to improve users'decision accuracy and confidence(Pu and Kumar 2004: Pu and Chen 2005). The Expert Clerk(Shimazu 2001), ATA (Automated Travel Assistant)(Linden et al 1997) and Smart client( Pu and Faltings 2000) were all exam- ples of such systems. Nguyen et al. 2004 realized the idea mainly to support on-tour recommendations for mobile users Such system is mainly composed of two components: a recommender agent that computes a set of k items that best match the user's current preference model, and a critiquing component that allows the user to actively create critiquing criteria and then examine a new set of k tradeoff alternatives. Expert Clerk and atA display three items at a time. whereas Smart Client returned seven items in its recent versions. Users can select any of the displayed items and navigate to products that offer tradeoff potentials. As for the critiquing aid, ExpertClerk provides a natural language dialog to request for users'feedback, ATA stated that it developed a graphical interface but without detailed description, and Smart Client has constantly improved the usability of its critiquing facility through user evaluations. We have chosen a latest version of Smart Client, called Example Critiquing, to explain the typical constructs of a k-item user-initiated critiquing system 2. 2. 1 Example critiquing Smart Client was originally developed as an online preference-based search tool for finding flights(Pu and Faltings 2000: Torrens et al. 2002). Its elementary model is he example-and-critiquing interaction, which was subsequently applied to product catalogs of vacation packages, insurance policies, apartments, and more recent com- mercial products such as tablet PCs and digital cameras(Pu and Faltings 2004; Pu and Kumar 2004; Chen and Pu 2006) In the latest Example Critiquing system, the recommendation part can be further divided into two sub-components: the first set of recommendations computed accord ng to the user's initial preferences, and the set of tradeoff alternatives recommended after each critiquing process. For example, for product catalogs of digital cameras and tablet PCs, k items(e. g,k= 7)are displayed in both cases. The number k was determined according to(Faltings et al. 2004)that discussed the optimal number of displayed solutions based on catalog sizes In the critiquing panel(see Fig 3), three radio buttons are next to each main feature facilitating users to choose to"keep"its value, "improve"it, or accept a compromised value suggested by the system (i.e, via"Take any suggestion). In particular, users can freely compose compound critiques by combining criteria on any set of mul- tiple features. The interface also supports users to perform simple similari critiquing(e.g,""show similar products with this one")by just keeping all current values, or define concrete value improvements on features(for example, under the "Improve"dropdown menu of price, there are options"S100 cheaper", ""$200 cheaper This kind of critiquing support has been also named as tradeoff assistance in some elated literatures(Pu and Kumar 2004; Chen and Pu 2006), since it is in nature to174 L. Chen, P. Pu over any combination of features with freedom. In fact, the purpose of this type of critiquing support is to assist users in freely executing tradeoff navigation, which is a process shown to improve users’ decision accuracy and confidence (Pu and Kumar 2004; Pu and Chen 2005). The ExpertClerk (Shimazu 2001), ATA (Automated Travel Assistant) (Linden et al. 1997) and SmartClient (Pu and Faltings 2000) were all exam￾ples of such systems. Nguyen et al.2004 realized the idea mainly to support on-tour recommendations for mobile users. Such system is mainly composed of two components: a recommender agent that computes a set of k items that best match the user’s current preference model, and a critiquing component that allows the user to actively create critiquing criteria and then examine a new set of k tradeoff alternatives. ExpertClerk and ATA display three items at a time, whereas SmartClient returned seven items in its recent versions. Users can select any of the displayed items and navigate to products that offer tradeoff potentials. As for the critiquing aid, ExpertClerk provides a natural language dialog to request for users’ feedback, ATA stated that it developed a graphical interface but without detailed description, and SmartClient has constantly improved the usability of its critiquing facility through user evaluations. We have chosen a latest version of SmartClient, called ExampleCritiquing, to explain the typical constructs of a k-item user-initiated critiquing system. 2.2.1 ExampleCritiquing SmartClient was originally developed as an online preference-based search tool for finding flights (Pu and Faltings 2000; Torrens et al. 2002). Its elementary model is the example-and-critiquing interaction, which was subsequently applied to product catalogs of vacation packages, insurance policies, apartments, and more recent com￾mercial products such as tablet PCs and digital cameras (Pu and Faltings 2004; Pu and Kumar 2004; Chen and Pu 2006). In the latest ExampleCritiquing system, the recommendation part can be further divided into two sub-components: the first set of recommendations computed accord￾ing to the user’s initial preferences, and the set of tradeoff alternatives recommended after each critiquing process. For example, for product catalogs of digital cameras and tablet PCs, k items (e.g., k = 7) are displayed in both cases. The number k was determined according to (Faltings et al. 2004) that discussed the optimal number of displayed solutions based on catalog sizes. In the critiquing panel (see Fig. 3), three radio buttons are next to each main feature, facilitating users to choose to “keep” its value, “improve” it, or accept a compromised value suggested by the system (i.e., via “Take any suggestion”). In particular, users can freely compose compound critiques by combining criteria on any set of mul￾tiple features. The interface also supports users to perform simple similarity-based critiquing (e.g., “show similar products with this one”) by just keeping all current values, or define concrete value improvements on features (for example, under the “Improve” dropdown menu of price, there are options “$100 cheaper”, “$200 cheaper”, etc.). This kind of critiquing support has been also named as tradeoff assistance in some related literatures (Pu and Kumar 2004; Chen and Pu 2006), since it is in nature to 123