5,413 research outputs found
A fuzzy associative classification approach for recommender systems
Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely
enriched our daily life by providing personalized suggestions and facilitating
people in decision-making, which makes them vital and indispensable to human
society in the information era. However, as people become more dependent on
them, recent studies show that recommender systems potentially own
unintentional impacts on society and individuals because of their unfairness
(e.g., gender discrimination in job recommendations). To develop trustworthy
services, it is crucial to devise fairness-aware recommender systems that can
mitigate these bias issues. In this survey, we summarise existing methodologies
and practices of fairness in recommender systems. Firstly, we present concepts
of fairness in different recommendation scenarios, comprehensively categorize
current advances, and introduce typical methods to promote fairness in
different stages of recommender systems. Next, after introducing datasets and
evaluation metrics applied to assess the fairness of recommender systems, we
will delve into the significant influence that fairness-aware recommender
systems exert on real-world industrial applications. Subsequently, we highlight
the connection between fairness and other principles of trustworthy recommender
systems, aiming to consider trustworthiness principles holistically while
advocating for fairness. Finally, we summarize this review, spotlighting
promising opportunities in comprehending concepts, frameworks, the balance
between accuracy and fairness, and the ties with trustworthiness, with the
ultimate goal of fostering the development of fairness-aware recommender
systems.Comment: 27 pages, 9 figure
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