Interaction design guidelines tatem argot c 的1材1 随m的,1m,22?m items(k=7) 品二 [ fnd slmA produets with bentar vaues than thi The product user selected to critique 2mM段1mMmm,1m User-initiated critiquing facility sam pes for creating unit or Osteal zoom compound Removable Flash Maman o te MB 2 Fig. 3 The Example Critiquing interfaces facilitate a user to specify tradeoff criteria: improving on one or several attributes that are important to her, while accepting compromised values on less important ones Tradeoff process involving only one feature(unit critique)or multiple features(com pound critique)are respectively termed as simple and complex tradeoffs by Pu and Kumar(2004) The search engine of computing recommended alternatives is adjusted for different decision environments. For configurable products, it employs sophisticated constraint satisfaction algorithms and models user preferences as soft constraints (Torrens et al 2002). For multi-attribute products, it is in theory grounded on the Weighted Additive sum rule (WADD), a compensatory decision strategy for explicitly resolving con cting values(Payne et al. 1993). As required by WADD, the users preferences are structured as a set of (attribute's acceptable value, relative importance)pairs After a user specifies her initial preferences, all alternatives will be ranked by their weighted utilities, and the top k items best matching the user's stated requirements willInteraction design guidelines 175 k recommended items (k = 7) The product user selected to critique User-initiated critiquing facility for creating unit or compound critiques Fig. 3 The ExampleCritiquing interfaces facilitate a user to specify tradeoff criteria: improving on one or several attributes that are important to her, while accepting compromised values on less important ones. Tradeoff process involving only one feature (unit critique) or multiple features (com￾pound critique) are respectively termed as simple and complex tradeoffs by Pu and Kumar (2004). The search engine of computing recommended alternatives is adjusted for different decision environments. For configurable products, it employs sophisticated constraint satisfaction algorithms and models user preferences as soft constraints (Torrens et al. 2002). For multi-attribute products, it is in theory grounded on the Weighted Additive sum rule (WADD), a compensatory decision strategy for explicitly resolving con- flicting values (Payne et al. 1993). As required by WADD, the user’s preferences are structured as a set of (attribute’s acceptable value, relative importance) pairs. After a user specifies her initial preferences, all alternatives will be ranked by their weighted utilities, and the top k items best matching the user’s stated requirements will 123