L Chen P Pu on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system. Keywords Critiquing-based recommender systems. Decision support Preference revision. User control. Example critiquing Dynamic critiquing. Hybrid critiquing User evaluation Usability. Human-computer interaction 1 Introduction According to adaptive decision theory(Payne et al. 1993), the human decision process is inherently highly constructive and adaptive to the current decision task and decision environment. In particular, when users are confronted with an unfamiliar product domain or a complex decision situation with overwhelming information, such as the current e-commerce environment, they are usually unable to accurately state their preferences at the outset( Viappiani et al. 2007) but likely construct them in a highly context-dependent fashion during their decision process(Tversky and Simonson 1993 Payne et al. 1999; Carenini and Poole 2002) In order to assist people in making accurate as well as confident decisions, espec in the complex decision setting, critiquing-based recommender systems have emerged in the form of both natural language models(Shimazu 2001; Thompson et al. 2004)and graphical user interfaces(Burke et al. 1996, 1997; Reilly et al. 2004; Pu and Kumar 2004). This type of system has been broadly recognized as an effective feedback mechanism that may guide users to efficiently target at their ideal products, which is particularly meaningful when users are searching for high-involvement products(e.g omputers, houses and cars) with the primary goal of avoiding any financial damage Other terms for these systems are conversational recommender systems(Smyth and McGinty 2003), conversational case-based reasoning systems(Shimazu 2001), and knowledge-based recommender systems(Burke et al. 1997; Burke 2000) More specifically, the critiquing-based recommender system mainly acts like an artificial salesperson that engages users in a conversational dialog where users can provide feedback in form of critiques(e.g, "I like this laptop, but prefer something heaper"or"with faster processor speed") to one of currently recommended items The feedback, in turn, enables the system to more accurately predict what the user truly wants and then return some products that may better interest the user in the next conversational cycle. The main component of this interaction model is therefore that of recommendation-and-critiquing, which is also called tweaking(Burke et al. 1997), critiquing feedback(Smyth and McGinty 2003), candidate/critiquing(Linden et al 1997), and navigation by proposing(Shimazu 2001) To our knowledge, the critiquing concept was first mentioned in the RABBITsystem (Williams and Tou 1982)as a new interface paradigm for formulating queries to a data- base. In recent years, it has evolved into two principal branches. One has been aimin to pro-actively generate a set of knowledge-based critiques that users may be prepared to accept as ways to improve the current pr this paper). This mechanism has been adopted in FindMe systems(Burke et al. 1997)168 L. Chen, P. Pu on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system. Keywords Critiquing-based recommender systems · Decision support · Preference revision · User control · Example critiquing · Dynamic critiquing · Hybrid critiquing · User evaluation · Usability · Human–computer interaction 1 Introduction According to adaptive decision theory (Payne et al. 1993), the human decision process is inherently highly constructive and adaptive to the current decision task and decision environment. In particular, when users are confronted with an unfamiliar product domain or a complex decision situation with overwhelming information, such as the current e-commerce environment, they are usually unable to accurately state their preferences at the outset (Viappiani et al. 2007) but likely construct them in a highly context-dependent fashion during their decision process (Tversky and Simonson 1993; Payne et al. 1999; Carenini and Poole 2002). In order to assist people in making accurate as well as confident decisions, especially in the complex decision setting, critiquing-based recommender systems have emerged in the form of both natural language models (Shimazu 2001; Thompson et al. 2004) and graphical user interfaces (Burke et al. 1996, 1997; Reilly et al. 2004; Pu and Kumar 2004). This type of system has been broadly recognized as an effective feedback mechanism that may guide users to efficiently target at their ideal products, which is particularly meaningful when users are searching for high-involvement products (e.g., computers, houses and cars) with the primary goal of avoiding any financial damage. Other terms for these systems are conversational recommender systems (Smyth and McGinty 2003), conversational case-based reasoning systems (Shimazu 2001), and knowledge-based recommender systems (Burke et al. 1997; Burke 2000). More specifically, the critiquing-based recommender system mainly acts like an artificial salesperson that engages users in a conversational dialog where users can provide feedback in form of critiques (e.g., “I like this laptop, but prefer something cheaper” or “with faster processor speed”) to one of currently recommended items. The feedback, in turn, enables the system to more accurately predict what the user truly wants and then return some products that may better interest the user in the next conversational cycle. The main component of this interaction model is therefore that of recommendation-and-critiquing, which is also called tweaking (Burke et al. 1997), critiquing feedback (Smyth and McGinty 2003), candidate/critiquing (Linden et al. 1997), and navigation by proposing (Shimazu 2001). To our knowledge, the critiquing concept was first mentioned in the RABBIT system (Williams and Tou 1982) as a new interface paradigm for formulating queries to a data￾base. In recent years, it has evolved into two principal branches. One has been aiming to pro-actively generate a set of knowledge-based critiques that users may be prepared to accept as ways to improve the current product (termed system-suggested critiquesin this paper). This mechanism has been adopted in FindMe systems (Burke et al. 1997) 123