L Chen P Pu navigate through the multi-dimensional space. An important interface component in FindMe is called tweaking, which allows users to critique the current recommendation by selecting one of the proposed simple tweaks(e. g, ""cheaper", "bigger"an When a user finds the current recommendation short of her expectations and responds to a tweak, the remaining candidates will be filtered to leave only those candidates satisfying the tweak c The critique suggestions in FindMe are called unit critiques since each of them constrains on a single feature at a time. More recently, a so-called dynamic critiquing method(Reilly et al. 2004; McCarthy et al. 2004a)has been developed with the objective of automatically generating a set of compound critiques, each of which can operate over multiple features simultaneously (e. g, ""Different Manufacture, Lower Resolution and Cheaper). A live-user trial showed that the integration of the dynamic critiquing method can effectively reduce users' intention cycles from an average of 29 in purely applying unit critiques to 6(McCarthy et al. 2005c). The compound critiques can also perform as explanations, revealing the ng recommendation opportunities except for the current product(Reilly et al. 2005). Therefore, we use the Dynamic Critiquing system as the representative to illustrate the main components lat a single-item system--suggested critiquing system may comprise 2.1.1 Dynamic Critiquing Figure 2 shows a sample Dynamic Critiquing interface where both unit and compound critiques are available to users as feedback options(Reilly et al. 2004; McCarthy et al. 2005c). It can be seen that the Dynamic Critiquing interface mainly contains three components: a single item as the current recommendation, a unit critiquing area and a list of compound critiques In the first recommendation cycle, an item that best matches the user's initially stated preferences is returned, and then after each critiquing action, a new item that satisfies the user's critique as well as being most similar to the previous recommended product will be shown as the current recommendation In the unit critiquing area, the system determines a set of main features, one of which users can choose to critique at a time. For each numerical feature(e. g, price). two critiquing directions are provided: increasing the value (e.g, more expensive)or decreasing it(e.g, cheaper), and for discrete features(e. g, manufacture) all of the relevant options are displayed under a drop-down menu. Therefore, this area performs more like a user-initiated unit critiquing support, rather than a limited small set of unit critique suggestions as in FindMe systems The list of three compound critiques are automatically computed by discoverin the recurring subsets of unit differences between the current recommended item and he remaining products using a data mining algorithm called Apriori(Agrawal et al 1993). More concretely, each remaining product, except the current recommendation, is first converted into a critique pattern indicating its differences from the current recommended product in terms of all main features(e.g,I(manufacture, =),(price <),(weight, >).)) Since there will be a number of critique patterns represent ing all of the remaining products, the Apriori algorithm is employed to discover the frequent association rules among features within these patterns. A set of compound172 L. Chen, P. Pu navigate through the multi-dimensional space. An important interface component in FindMe is called tweaking, which allows users to critique the current recommendation by selecting one of the proposed simple tweaks (e.g., “cheaper”, “bigger” and “nicer”). When a user finds the current recommendation short of her expectations and responds to a tweak, the remaining candidates will be filtered to leave only those candidates satisfying the tweak. The critique suggestions in FindMe are called unit critiques since each of them constrains on a single feature at a time. More recently, a so-called dynamic critiquing method (Reilly et al. 2004; McCarthy et al. 2004a) has been developed with the objective of automatically generating a set of compound critiques, each of which can operate over multiple features simultaneously (e.g., “Different Manufacture, Lower Resolution and Cheaper”). A live-user trial showed that the integration of the dynamic critiquing method can effectively reduce users’ intention cycles from an average of 29 in purely applying unit critiques to 6 (McCarthy et al. 2005c). The compound critiques can also perform as explanations, revealing the remaining recommendation opportunities except for the current product (Reilly et al. 2005). Therefore, we use the DynamicCritiquing system as the representative to illustrate the main components that a single-item system-suggested critiquing system may comprise. 2.1.1 DynamicCritiquing Figure 2 shows a sample DynamicCritiquing interface where both unit and compound critiques are available to users as feedback options (Reilly et al. 2004; McCarthy et al. 2005c). It can be seen that the DynamicCritiquing interface mainly contains three components: a single item as the current recommendation, a unit critiquing area and a list of compound critiques. In the first recommendation cycle, an item that best matches the user’s initially stated preferences is returned, and then after each critiquing action, a new item that satisfies the user’s critique as well as being most similar to the previous recommended product will be shown as the current recommendation. In the unit critiquing area, the system determines a set of main features, one of which users can choose to critique at a time. For each numerical feature (e.g., price), two critiquing directions are provided: increasing the value (e.g., more expensive) or decreasing it (e.g., cheaper), and for discrete features (e.g., manufacture) all of the relevant options are displayed under a drop-down menu. Therefore, this area performs more like a user-initiated unit critiquing support, rather than a limited small set of unit critique suggestions as in FindMe systems. The list of three compound critiques are automatically computed by discovering the recurring subsets of unit differences between the current recommended item and the remaining products using a data mining algorithm called Apriori (Agrawal et al. 1993). More concretely, each remaining product, except the current recommendation, is first converted into a critique pattern indicating its differences from the current recommended product in terms of all main features (e.g., {(manufacture, =), (price, <), (weight, >),…}). Since there will be a number of critique patterns represent￾ing all of the remaining products, the Apriori algorithm is employed to discover the frequent association rules among features within these patterns. A set of compound 123