Interaction design guidelines Fig 4 User evaluation framework for critiquing-based Critiquing- Based Recommender Systems Decision Effort Objective Accuracy Perceived Accuracy Perceived Efort Intention to Purchase Intention to Return 4.1 Decision accuracy The foremost criterion of evaluating a recommender system should be the decision accuracy that it enables users to eventually achieve. If a user can target her ideal choice with the system, it means that the system assisted her in reaching 100% decision accuracy. In our experiments, we not only measured the objective accuracy that a participant may obtain, but also her subjectively perceived accuracy (i. e, confidence in choice) Objective accuracy The objective accuracy was quantitatively measured by the fraction of partici who switched to a different, better option than the one chosen with the system, when they were asked to view all alternatives in the database. This procedure is known as the switching task, and has been practically applied by researchers in marketing science to measure consumers' decision quality(Haubl and Trifts 2000). A lower switchin fraction means that the system supports higher decision accuracy since most of users stood by their choice with it. On the contrary, a higher switching fraction implies hat the recommender is not very capable of guiding users to locate what they truly antFor expensive products, inaccurate tools may cause both financial damage and emotional burden to a decision maker Besides objective accuracy, we also measured the degree of accuracy users subjec tively perceived while using the system, which is also called decision confidence (Pu and Kumar 2004). The confidence judgment may potentially impact on users perception of the system's competence and even their intention to purchase the chosen product. This variable was quantitatively assessed by asking subjects to respond to a statement(. g, "I am confident that the product I just'purchased' is really the bestInteraction design guidelines 179 Fig. 4 User evaluation framework for critiquing-based recommender systems Trusting Intentions Decision Effort Objective Effort Perceived Effort Decision Accuracy Intention to Purchase Intention to Return Critiquing-Based Recommender Systems Objective Accuracy Perceived Accuracy 4.1 Decision accuracy The foremost criterion of evaluating a recommender system should be the decision accuracy that it enables users to eventually achieve. If a user can target her ideal choice with the system, it means that the system assisted her in reaching 100% decision accuracy. In our experiments, we not only measured the objective accuracy that a participant may obtain, but also her subjectively perceived accuracy (i.e., confidence in choice). Objective accuracy The objective accuracy was quantitatively measured by the fraction of participants who switched to a different, better option than the one chosen with the system, when they were asked to view all alternatives in the database. This procedure is known as the switching task, and has been practically applied by researchers in marketing science to measure consumers’ decision quality (Häubl and Trifts 2000). A lower switching fraction means that the system supports higher decision accuracy since most of users stood by their choice with it. On the contrary, a higher switching fraction implies that the recommender is not very capable of guiding users to locate what they truly want. For expensive products, inaccurate tools may cause both financial damage and emotional burden to a decision maker. Perceived accuracy Besides objective accuracy, we also measured the degree of accuracy users subjec￾tively perceived while using the system, which is also called decision confidence (Pu and Kumar 2004). The confidence judgment may potentially impact on users’ perception of the system’s competence and even their intention to purchase the chosen product. This variable was quantitatively assessed by asking subjects to respond to a statement (e.g., “I am confident that the product I just ‘purchased’ is really the best 123