Journal ArticleDOI
Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects
TLDR
Results provide strong evidence that the rating presented by a recommender system serves as an anchor for the consumer's constructed preference, and have a number of important implications on recommender systems performance metrics and design, preference bias, potential strategic behavior, and trust, which are discussed.Abstract:
Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, although behavioral aspects of using recommender systems are often overlooked. In our studies, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. We conducted three controlled laboratory experiments to explore the effects of system recommendations on preferences. Studies 1 and 2 investigated user preferences for television programs across a variety of conditions, which were surveyed immediately following program viewing. Study 3 investigated the granularity of the observed effects within individual participants. Results provide strong evidence that the rating presented by a recommender system serves as an anchor for the consumer's constructed preference. Viewers' preference ratings are malleable and can be significantly influenced by the recommendation received. The effect is sensitive to the perceived reliability of a recommender system and, thus, not a purely numerical or priming-based effect. Finally, the effect of anchoring is continuous and linear, operating over a range of perturbations of the system. These general findings have a number of important implications e.g., on recommender systems performance metrics and design, preference bias, potential strategic behavior, and trust, which are discussed.read more
Citations
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Journal ArticleDOI
Effects of Traditional Advertising and Social Messages on Brand-Building Metrics and Customer Acquisition
TL;DR: In this article, the relative effectiveness of traditional advertising, impressions generated through firm-to-consumer (F2C) messages on Facebook, and the volume and valence of C2C messages on Twitter and web forums for brand-building and customer acquisition efforts was examined.
Journal ArticleDOI
Research Commentary—Informing Privacy Research Through Information Systems, Psychology, and Behavioral Economics: Thinking Outside the “APCO” Box
TL;DR: This work proposes an enhanced APCO model and a set of related propositions that consider both deliberative and low-effort cognitive responses inspired by frameworks and theories in behavioral economics and psychology that also affect privacy decisions.
Journal ArticleDOI
Interacting with Recommenders—Overview and Research Directions
Michael Jugovac,Dietmar Jannach +1 more
TL;DR: This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems, covering existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process.
Journal ArticleDOI
Are customers reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning
TL;DR: The results from fixed effects regression models show that online ratings from user-generated reviews on TripAdvisor have a positive effect on hotel revenue growth that is outweighed by a negative effect on gross profit margins, shifting hotel competition from unit profit margin to volumes and to higher room occupancy rates.
References
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Book
Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
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