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Recommender systems are present in many web applications to guide our choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products is questionable. Here we introduce a model to examine the benefit of recommender systems for users, and found that recommendations from the system can be equivalent to random draws if one relies too strongly on the system. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some algorithms. On the other hand, we found that a high accuracy evaluated by common accuracy metrics does not necessarily correspond to a high real accuracy nor a benefit for users, which serves as an alarm for operators and researchers of recommender systems. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems can benefit users, but relying too strongly on the system may render the system ineffective.
An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50 percent, or just half of their student populati
We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deplo
Investor attention is an important concept in behavioral finance. Many articles have conducted cross-disciplinary research leading by this concept. In this paper, we use data extraction technology to collect a large number of Baidu Index keyword sear
Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of
The swift transitions in higher education after the COVID-19 outbreak identified a gap in the pedagogical support available to faculty. We propose a smart, knowledge-based chatbot that addresses issues of knowledge distillation and provides faculty w