Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDo recommender systems benefit users?
117
0
0.0
(
0
)
Added by
Chi Ho Yeung
Publication date
2015
fields
Informatics Engineering
and research's language is
English
Authors
Chi Ho Yeung
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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 populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Multi-Linear Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.
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 deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.
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 search volume data. After analyzing the data, we draw a conclusion that has not been paid attention to in all the past research. We find heterogeneity in searching by internet users in China. Firstly, in terms of search behavior, internet users are more inclined to use the PC end to obtain information when facing areas which need to be taken seriously by them. Secondly, attention is heterogeneous while searching. When Internet users search for information in mobile end, their attention is divergent, and search for seemingly unrelated keywords at the same time which limits their attention to information.
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 its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.
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 with personalized recommendations. Our collaborative system crowdsources useful pedagogical practices and continuously filters recommendations based on theory and user feedback, thus enhancing the experiences of subsequent peers. We build a prototype for our local STEM faculty as a proof concept and receive favorable feedback that encourages us to extend our development and outreach, especially to underresourced faculty.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
