(Q56341827)

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A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems

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A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems (English)

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Joeran Beel

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author name string

Stefan Langer

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2

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English

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publication date

2015

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published in

Lecture Notes in Computer Science

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page(s)

153-168

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cites work

Why batch and user evaluations do not give the same results

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https://api.crossref.org/works/10.1007%2F978-3-319-24592-8_12

retrieved

21 January 2018

What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects

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21 January 2018

Evaluating collaborative filtering recommender systems

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21 January 2018

A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation

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Crossref

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https://api.crossref.org/works/10.1007%2F978-3-319-24592-8_12

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21 January 2018

Countering method bias in questionnaire‐based user studies

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21 January 2018

Docear: an academic literature suite for searching, organizing and creating academic literature

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21 January 2018

The Architecture and Datasets of Docear's Research Paper Recommender System

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21 January 2018

Introducing Docear's research paper recommender system

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21 January 2018

Exploring the Potential of User Modeling Based on Mind Maps

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21 January 2018

Utilizing Mind-Maps for Information Retrieval and User Modelling

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21 January 2018

Research-paper recommender systems: a literature survey

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31 March 2020

Research paper recommender system evaluation

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https://api.crossref.org/works/10.1007%2F978-3-319-24592-8_12

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31 March 2020

Docear's PDF inspector: title extraction from PDF files

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31 March 2020

User Modeling via Stereotypes*

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31 March 2020

Identifiers

DOI

10.1007/978-3-319-24592-8_12

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DBLP publication ID

conf/ercimdl/BeelL15

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DBLP Dataset 2021-01-02

retrieved

28 January 2021

ResearchGate publication ID

283862818

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(Q56341827)

A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems

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