The performance of recommender systems in online shopping: A user-centric study

•We evaluate four approaches to online product search in an extensive user study with real data.•The approaches that we study exploit novel preference relaxation methods to recommend products.•We compare the results of a user experiment with simulations, using two large sets of products.•We show that a particular preference relaxation method improves the decisions of online shoppers.

This research investigates the effects of preference relaxation on decision-making performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45,278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the Soft-Boundary Preference Relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.