The main contribution of this paper is improving the quality of recommendations and addressing sparsity problem using genetic algorithm and a multidimensional information model
Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model
Egyptian Informatics Journal, no. 1 (2013): 67-78
In recent years, the explosion of learning materials in the web-based educational systems has caused difficulty of locating appropriate learning materials to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable materials to learners...更多
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- With the growth of technology in educational organizations at recent years, Web-based learning environments are becoming very popular.
- A recommender system in an e-learning context is a software agent that tries to ’’intelligently’’ recommend actions to a learner based on the actions of previous learners
- This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be a web material .
- By using material recommender systems in learning environments, the authors can address two problems, personalization and information overload.
- Recommender system offers which learning objects should learners study , or offers learning objects in order to contribute to the learners’ progress towards particular goals 
- With the growth of technology in educational organizations at recent years, Web-based learning environments are becoming very popular
- In order to check the performance of the proposed algorithm, a real-world dataset is applied in our simulations
- MACE1 dataset that is pan-European initiative to interconnect and disseminate digital information about architecture is used for experiment
- Since the repository of learning materials is very massive and these materials have several attributes, there are several drawbacks such as sparsity when applying the existing recommendation algorithms. To address these problems and have a good recommendation for learner, this paper presents a novel personalized recommender system that utilizes explicit and implicit attributes of materials in the unified model
- The experiment results show that the proposed approach performs better than the traditional approaches
- The main contribution of this paper is improving the quality of recommendations and addressing sparsity problem using genetic algorithm and a multidimensional information model
- Evaluation metrics and data set
In order to check the performance of the proposed algorithm, a real-world dataset is applied in the simulations.
- MACE1 dataset that is pan-European initiative to interconnect and disseminate digital information about architecture is used for experiment.
- This dataset is issued from MACE project that is done from September 2006 to September 2010.
- The precision and recall are most popular metrics that evaluate decision support accuracy.
- Several ways to evaluate precision and recall exists .
- When referring to Recommender Systems the recall can be defined as follows: Recall jtest
- One of the most important applications of recommendation systems in e-learning environment is personalization and recommendation of learning materials.
- Since the repository of learning materials is very massive and these materials have several attributes, there are several drawbacks such as sparsity when applying the existing recommendation algorithms
- To address these problems and have a good recommendation for learner, this paper presents a novel personalized recommender system that utilizes explicit and implicit attributes of materials in the unified model.
- Using these sequential patterns, the authors can predict the most probable resource that a learner will access in near feature
- Table1: A comparison of prediction accuracy of various methods
- Since increasing the size of the recommendation set leads to an increase in recall but at the same time a decrease in precision, we can use F1 measure  that is a well-known combination metric with the following formula: F1
This dataset is issued from MACE project that is done from September 2006 to September 2010. This dataset contains 1148 learners and 12,000 materials. The precision and recall are most popular metrics that evaluate decision support accuracy
users with the average number of ratings about 100: 500
The results of user-based and proposed method obtained from the same data set. Comparisons were produced for N = 500 users with the average number of ratings about 100, and M = 50. As can be seen, the proposed multi-attribute based method has better prediction accuracy of the memorybased, mixture PLSA method and other methods in terms of MAE
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