Multi-method Evaluation in Scientific Paper Recommender Systems

Raamkumar, Aravind Sesagiri; Foo, Schubert

doi:10.1145/3213586.3226215

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The origins of recommendation systems lie in the research areas o information retrieval, cognitive science, and machine learning [10]. Most of the studies on recommendation systems, such as [11][12][13], address the underlying procedures used. Since research in this area began, various recommendation algorithms have been proposed and tested.…”

Section: State Of the Art: Recsys And Collaborative Filtering Processesmentioning

confidence: 99%

RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Azeroual

Koltay

2022

Publications

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Recommendation (recommender) systems have played an increasingly important role in both research and industry in recent years. In the area of publication data, for example, there is a strong need to help people find the right research information through recommendations and scientific reports. The difference between search engines and recommendation systems is that search engines help us find something we already know, while recommendation systems are more likely to help us find new items. An essential function of recommendation systems is to support users in their decision making. Recommendation systems are information systems that can be categorized into decision support systems, as long as they are used for decision making and are intended to support people instead of replacing them. This paper deals with recommendation systems for research information, especially publication data. We discuss and analyze the challenges and peculiarities of implementing recommender systems for the scientific exchange of research information. For this purpose, data mining techniques are examined and a concept for a recommendation system for research information is developed. Our aim is to investigate to what extent a recommendation system based on a collaborative filtering approach with cookies is possible. The data source is publication data extracted from cookies in the Web of Science database. The results of our investigation show that a collaborative filtering process is suitable for publication data and that recommendations can be generated with user information. In addition, we have seen that collaborative filtering is an important element that can solve a practical problem by sifting through large amounts of dynamically generated information to provide users with personalized content and services.

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Section: State Of the Art: Recsys And Collaborative Filtering Processesmentioning

confidence: 99%

RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Azeroual

Koltay

2022

Publications

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“…The number of algorithms, applications, and studies is so large that some scientists affirm we are living in the age of recommender systems [1]. Their techniques are nowadays used in many domains, from movies [2] and books [3], to scientific papers [4], fitness training [5] and even friends [6], it seems everything can be suggested by these tools. However, we have not seen much use of recommender systems to assist humans in design tasks.…”

Section: Introductionmentioning

confidence: 99%

Pitako - Recommending Game Design Elements in Cicero

Machado

Gopstein

Nealen

et al. 2019

2019 IEEE Conference on Games (CoG)

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Recommender Systems are widely and successfully applied in e-commerce. Could they be used for design? In this paper, we introduce Pitako 1 , a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixedinitiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

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A review of citation recommendation: from textual content to enriched context

Zhang

Liu

2020

Scientometrics

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Multi-method Evaluation in Scientific Paper Recommender Systems

Cited by 4 publications

References 7 publications

RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Pitako - Recommending Game Design Elements in Cicero

A review of citation recommendation: from textual content to enriched context

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