User Model User-Adap Inter(2009)19: 167-206 DOI10.1007/s11257-008-9057-x ORIGINAL PAPER Interaction design guidelines on critiquing-based recommender systems Li Chen· Pearl Pu Received: 19 September 2007/ Accepted in revised form: 25 August 2008/ Published online: 3 October 2008 O Springer Science+Business Media B V. 2008 Abstract A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the users preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing based recommender system: critiquing coverage--one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid- system-suggested critiques(i. e, a set of critique suggestions for users to select)vs user-initiated critiquing facility (i.e, facilitating users to create critiques on their own) Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items(as opposed to just one) has significantly positive impacts on increasing users' decision accuracy(particularly in the first recommendation cycle)and saving their objective effort(in the later critiquing ycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light L Chen(四)·PP Human Computer Interaction Group, School of Computer and Communication Sciences Swiss Federal Institute of Technology in Lausanne(EPFL), 1015 Lausanne, Switzerland e-mail: li chen( epfl. ch P Pu e-mail: pearl- pu(@epfl. chUser Model User-Adap Inter (2009) 19:167–206 DOI 10.1007/s11257-008-9057-x ORIGINAL PAPER Interaction design guidelines on critiquing-based recommender systems Li Chen · Pearl Pu Received: 19 September 2007 / Accepted in revised form: 25 August 2008 / Published online: 3 October 2008 © Springer Science+Business Media B.V. 2008 Abstract A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user’s preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user’s interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing￾based recommender system: critiquing coverage—one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid— system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users’ decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users’ decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light L. Chen (B) · P. Pu Human Computer Interaction Group, School of Computer and Communication Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland e-mail: li.chen@epfl.ch P. Pu e-mail: pearl.pu@epfl.ch 123