Personal Recommender Simulation

Bert Van den Berg; Rob Nadolski; Hendrik Drachsler; Adriana Berlanga; Hans Hummel; Rob Koper; Peter Sloep

Personal Recommender Simulation

Bert Van den Berg, Rob Nadolski, Hendrik Drachsler, Adriana Berlanga, Hans Hummel, Rob Koper, Peter Sloep

Research output: Non-textual form › Software › Academic

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Abstract

The main goal of our project is to research and develop technologies that support users in Learning Networks. One of the questions in this research is how to tackle the problem of the difficulty for learners to navigate through the network: what is the most effective way to reach the learning target or which course can be studied best after finishing a particular one? The aim of this program is to simulate recommendations (strategies) of learning actions (LA's) for learners in a learning network with different (sub) domains, targets, preferences and competences. It is used to explore and to research the effectiveness of different recommendation strategies in various settings.

Original language	English
Publication status	Published - 22 Mar 2008

Keywords

simulation
recommender strategies
collaborative filtering
rating

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Statistic filesAccepted author manuscript, 7.56 MB

http://ou-dspace.library.maastrichtuniversity.nl/dspace/12_ln_software_040272018/2/prs.nlogo

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title = "Personal Recommender Simulation",

abstract = "The main goal of our project is to research and develop technologies that support users in Learning Networks. One of the questions in this research is how to tackle the problem of the difficulty for learners to navigate through the network: what is the most effective way to reach the learning target or which course can be studied best after finishing a particular one? The aim of this program is to simulate recommendations (strategies) of learning actions (LA's) for learners in a learning network with different (sub) domains, targets, preferences and competences. It is used to explore and to research the effectiveness of different recommendation strategies in various settings.",

keywords = "simulation, recommender strategies, collaborative filtering, rating",

author = "{Van den Berg}, Bert and Rob Nadolski and Hendrik Drachsler and Adriana Berlanga and Hans Hummel and Rob Koper and Peter Sloep",

note = "DS_Description: The simulation and its background is described in detail in: Simulating [in the search] for lightweight Personalised Recommendation Systems in Learning Networks: A case for Pedagogy-Oriented Rating-based Hybrid Recommendation Strategies Rob J. Nadolski, Bert van den Berg, Adriana J. Berlanga, Hendrik Drachsler, Hans G.K. Hummel, Rob Koper and Peter B. Sloep. Open University of the Netherlands. Available under the three clause BSD licence, Copyright TENCompetence Foundation.",

year = "2008",

month = mar,

day = "22",

language = "English",

}

TY - ADVS

T1 - Personal Recommender Simulation

AU - Van den Berg, Bert

AU - Nadolski, Rob

AU - Drachsler, Hendrik

AU - Berlanga, Adriana

AU - Hummel, Hans

AU - Koper, Rob

AU - Sloep, Peter

N1 - DS_Description: The simulation and its background is described in detail in: Simulating [in the search] for lightweight Personalised Recommendation Systems in Learning Networks: A case for Pedagogy-Oriented Rating-based Hybrid Recommendation Strategies Rob J. Nadolski, Bert van den Berg, Adriana J. Berlanga, Hendrik Drachsler, Hans G.K. Hummel, Rob Koper and Peter B. Sloep. Open University of the Netherlands. Available under the three clause BSD licence, Copyright TENCompetence Foundation.

PY - 2008/3/22

Y1 - 2008/3/22

N2 - The main goal of our project is to research and develop technologies that support users in Learning Networks. One of the questions in this research is how to tackle the problem of the difficulty for learners to navigate through the network: what is the most effective way to reach the learning target or which course can be studied best after finishing a particular one? The aim of this program is to simulate recommendations (strategies) of learning actions (LA's) for learners in a learning network with different (sub) domains, targets, preferences and competences. It is used to explore and to research the effectiveness of different recommendation strategies in various settings.

AB - The main goal of our project is to research and develop technologies that support users in Learning Networks. One of the questions in this research is how to tackle the problem of the difficulty for learners to navigate through the network: what is the most effective way to reach the learning target or which course can be studied best after finishing a particular one? The aim of this program is to simulate recommendations (strategies) of learning actions (LA's) for learners in a learning network with different (sub) domains, targets, preferences and competences. It is used to explore and to research the effectiveness of different recommendation strategies in various settings.

KW - simulation

KW - recommender strategies

KW - collaborative filtering

KW - rating

M3 - Software

ER -

Personal Recommender Simulation

Abstract

Keywords

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