Recommender Systems, Consumer Preferences, and Anchoring Effects
نویسندگان
چکیده
Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve the accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In this study, 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, which were surveyed immediately following program viewing. Study 3 broadened to an additional context—preferences for jokes. Results provide strong evidence viewers’ preferences are malleable and can be significantly influenced by ratings provided by recommender systems. Additionally, the effects of pure number-based anchoring can be separated from the effects of the perceived reliability of a recommender system. Finally, the effect of anchoring is roughly continuous, operating over a range of perturbations of the system.
منابع مشابه
Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects
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, while behavioral aspects of using recommender systems are often overlooked. In this study, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. W...
متن کاملImpact of Recommender Systems on Consumer Preferences: A Study of Anchoring Effects
Recommendation systems have become prevalent decision aids in the electronic marketplace and an integral part of the business models of many firms. Such systems provide suggestions to the consumer as to products in which they may be interested and allow the firms to leverage the power of collaborative filtering and feature-based recommendations to better serve their customers and increase sales...
متن کاملDe-Biasing User Preference Ratings in Recommender Systems
Prior research has shown that online recommendations have significant influence on users’ preference ratings and economic behavior. Specifically, the self-reported preference rating (for a specific consumed item) that is submitted by a user to a recommender system can be affected (i.e., distorted) by the previously observed system’s recommendation. As a result, anchoring (or anchoring-like) bia...
متن کاملA social recommender system based on matrix factorization considering dynamics of user preferences
With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems...
متن کاملImproving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data
The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of us...
متن کامل