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Partitioning Sorted Sets: Overcoming Choice Overload while Maintaining Decision Quality

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TLDR
In this paper, the authors investigated the joint use of partitioning and sorting as a choice architecture to overcome consumer choice overload in large product sets and proposed a practical approach to select partitioning size depending on sorting quality.
Abstract
textWe investigate the joint use of partitioning and sorting as a choice architecture to overcome consumer choice overload in large product sets. Partitioning first presents a small initial set of alternatives with the option to click through to see the remaining alternatives. Sorting presents alternatives in order of attractiveness based on a user model that is helpful to the decision-maker. We propose that Sets with Partitioning and Sorting (SPSs) improve consumers’ choice outcomes by increasing their focus on the most attractive alternatives and their use of more compensatory decisions. Results from two controlled survey-based experiments and a field study in the domain of health insurance support this positive impact of SPSs when sorting quality is high. However, there is also a potential harmful effect of partitioning when sorting quality is low. We discuss implications of our findings and propose a practical approach to select partitioning size depending on sorting quality.

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Choice overload and recommendation effectiveness in related-article recommendations

TL;DR: This work examines choice overload when displaying related-article recommendations in digital libraries, and examines the effectiveness of recommendation algorithms in this domain, finding that with increasing recommendation set size, i.e., the numbers of displayed recommendations, CTR decreases from 0.41% for one recommendation to 0.09% for 15 recommendations.
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When more is less: The other side of artificial intelligence recommendation

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