Dataset Information

Uncovering the information core in recommender systems.

ABSTRACT: With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommendation efficiency by excluding irrelevant users. In this paper, we argue that in each online system there exists a group of core users who carry most of the information for recommendation. With them, the recommender systems can already generate satisfactory recommendation. Our core user extraction method enables the recommender systems to achieve 90% of the accuracy of the top-L recommendation by taking only 20% of the users into account. A detailed investigation reveals that these core users are not necessarily the large-degree users. Moreover, they tend to select high quality objects and their selections are well diversified.

SUBMITTER: Zeng W

PROVIDER: S-EPMC4139954 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Uncovering the information core in recommender systems.

Zeng Wei W Zeng An A Liu Hao H Shang Ming-Sheng MS Zhou Tao T

Scientific reports 20140821

With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommen ...[more]

PMID: 25142186

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Project description:Understanding user's behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems. For instance, in the Recommender Systems domain, the accuracy of the recommendation algorithm was the ultimate goal for most systems designers. However, researchers and practitioners have realized that providing accurate recommendations is insufficient to enhance users' acceptance. A recommender system needs to focus on other factors that enhance its interactions with the users. Recent researches suggest augmenting these systems with persuasive capabilities. Persuasive features lead to increasing users' acceptance of the recommendations, which, in turn, enhances users' experience with these systems. Nonetheless, the literature still lacks a comprehensive view of the actual effect of persuasive principles on recommender users. To fill this gap, this study diagnoses how users of different characteristics get influenced by various persuasive principles that a recommender system uses. The study considers four users' aspects: age, gender, culture (continent), and personality traits. The paper also investigates the impact of the context (or application domain) on the influence of the persuasive principles. Two application domains (namely eCommerce and Movie recommendations) are considered. A within-subject user study was conducted. The analysis of (279) responses revealed that persuasive principles have the potential to enhance users' experience with recommender systems. The study also shows that, among the considered factors, culture, personality traits, and the domain of recommendations have a higher impact on the influence of persuasive principles than other factors. Based on the analysis of the results, the study provides insights and guidelines for recommender systems designers. These guidelines can be used as a reference for designing recommender systems with users' experience in mind. We suggest that considering the results presented in this paper could help to improve recommender-users interaction.

Project description:BackgroundPeople increasingly rely on online health information for their health-related decision-making. Given the overwhelming amount of information available, the risk of misinformation is high. Health recommender systems, which recommend personalised health-related information or interventions using intelligent algorithms, have the potential to address this issue. Many such systems have been developed and evaluated individually, but there is a need to synthesise the evaluation findings to identify gaps and ensure that future recommender systems are designed to have a positive impact on health or target behaviours.ObjectiveThe purpose of this review is to provide an overview of the state of the literature evaluating health recommender systems and highlight lessons learnt, methodological considerations and gaps in current research.Methods and analysisThe review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and the Population, Concept, and Context frameworks. Five databases (PubMED, ACM Digital Library Full-Text Collection, IEEE Xplore, Web of Science and ScienceDirect) will be searched for studies published in English that evaluate at least one health recommender system using search terms following the themes reflecting digital health, recommendation systems and evaluations of efficacy and impact. After using EndNote 21 for initial screening, two independent reviewers will screen the titles, abstracts and full texts of the references, and then extract data from included studies related to the recommender system characteristics, evaluation design and evaluation findings into a predetermined form. A descriptive analysis will be conducted to provide an overview of the literature; key themes and gaps in the literature will be discussed.Ethics and disseminationEthical approval is not required as data will be obtained from already published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

Dataset Information

Uncovering the information core in recommender systems.

Publications

Uncovering the information core in recommender systems.

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