Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering
Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering
复制标题
DOI：
10.1007/s10639-012-9245-5
复制
发表时间：
2014-12-01
影响因子：
5.5
通讯作者：
Ghoushchi, Mohammad Bagher Ghaznavi
中科院分区：
文献类型：
--
作者：
Salehi, Mojtaba;Kamalabadi, Isa Nakhai;Ghoushchi, Mohammad Bagher Ghaznavi
材料推荐系统是电子学习系统的重要组成部分，用于向学习者个性化和推荐适当的材料。然而，现有的推荐算法没有充分考虑学习者的动态兴趣和多偏好以及材料的多维属性。此外，这些算法无法在推荐中有效地利用学习者历史上的材料访问顺序模式。为了解决这些问题，提高推荐的准确性和质量，提出了一种基于顺序模式挖掘和基于多维属性的协同过滤（CF）的新材料推荐系统框架。在基于顺序模式的方法中，实施修改的 Apriori 和 PrefixSpan 算法来发现材料访问中的潜在模式并将其用于推荐。在基于多维属性的 CF 方法中，引入了学习者偏好树 (LPT)，以考虑材料的多维属性以及学习者的评分和模型动态以及学习者的多偏好。最后，使用级联、加权和混合方法组合两种方法的推荐结果。该方法在分类精度指标上优于以往的算法，并且可以根据实时更新的上下文信息准确地满足学习者的真实学习偏好。
Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover, these algorithms cannot effectively use the learner's historical sequential patterns of material accessing in recommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach, modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners' rating and model dynamic and multi-preference of learners in the multidimensional attribute-based CF approach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learner's real learning preference can be satisfied accurately according to the real-time up dated contextual information.