基于改进自编码器的在线课程推荐模型
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中国石油大学(华东)教学研究与改革项目(QT-202005)


Online Course Recommendation Model Based on Enhanced Auto-encoder
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    摘要:

    随着互联网技术的发展以及2020年新冠疫情的爆发, 越来越多的学生选择在线教育. 然而在线课程数量庞大, 往往无法及时找到合适的课程, 个性化智能推荐系统是解决这一问题的有效方案. 本文根据用户在线学习具有明显时序性的特点, 提出一种基于改进自编码器的在线课程推荐模型. 首先, 利用长短期记忆网络改进自编码器, 使得模型可以提取数据的时序性特征; 然后, 利用Softmax函数进行课程的推荐. 实验结果表明, 所提方法与协同过滤算法和基于传统自编码器的推荐模型相比, 具有更高的推荐准确率.

    Abstract:

    With the development of Internet technology and the outbreak of COVID-19 in 2020, more and more students have chosen online education. However, due to the large number of online courses, students are often unable to find suitable courses in time. A personalized intelligent recommendation system is an effective solution to this problem. Considering the obvious sequential characteristics of users for online learning, an online course recommendation model based on enhanced auto-encoders is proposed. First, the auto-encoder is enhanced with the long short-term memory network, so the model can extract the sequential characteristics of data. Then, the Softmax function is used to recommend online courses. Experimental results show that the proposed method has higher recommendation accuracy than the collaborative filtering algorithm and the recommendation model based on traditional auto-encoders.

    参考文献
    [1] Zhang S, Yao LN, Sun AX, et al. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 2019, 52(1): 5
    [2] Yang B, Lei Y, Liu JM, et al. Social collaborative filtering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633–1647.
    [3] Strub F, Gaudel R, Mary J. Hybrid recommender system based on autoencoders. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston: ACM, 2016. 11–16.
    [4] 陈征, 宋轩, 杜先瑞, 等. 基于多图神经网络的个性化推荐模型. 中国传媒大学学报(自然科学版), 2020, 27(6): 14–18.
    [5] 郭旦怀, 张鸣珂, 贾楠, 等. 融合深度学习技术的用户兴趣点推荐研究综述. 武汉大学学报(信息科学版), 2020, 45(12): 1890–1902.
    [6] 徐亚军, 郭俭. K12学习平台个性化学习资源推荐. 计算机系统应用, 2020, 29(7): 217–221.
    [7] Strub F, Mary J. Collaborative filtering with stacked denoising autoencoders and sparse inputs. NIPS Workshop on Machine Learning for eCommerce. Montreal, 2015.
    [8] 刘国丽, 廉孟杰, 于丽梅, 等. 融合专家信任的协同过滤推荐算法. 计算机系统应用, 2021, 30(4): 160–167.
    [9] Li J, Ye Z. Course recommendations in online education based on collaborative filtering recommendation algorithm. Complexity, 2020, 2020: 6619249
    [10] Cui ZH, Xu XH, Xue F, et al. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing, 2020, 13(4): 685–695.
    [11] 王素琴, 吴子锐. 利用LSTM网络和课程关联分类的推荐模型. 计算机科学与探索, 2019, 13(8): 1380–1389.
    [12] 卜祥鹏. 基于GRU和课程关联关系的推荐模型. 软件, 2020, 41(6): 137–142.
    [13] Sedhain S, Menon AK, Sanner S, et al. Autorec: Autoencoders meet collaborative filtering. Proceedings of the 24th International Conference on World Wide Web. Florence: ACM, 2015. 111–112.
    [14] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780.
    [15] Graves A, Liwicki M, Fernández S, et al. A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(5): 855–868.
    [16] Sak H, Senior A, Beaufays F. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv: 1402.1128, 2014.
    [17] Malhotra P, Vig L, Shroff G, et al. Long short term memory networks for anomaly detection in time series. Proceedings, vol. 89. Presses Universitaires de Louvain, 2015. 89–94.
    [18] Staudemeyer RC, Morris ER. Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv: 1909.09586, 2019.
    [19] Cho K, Van Merri?nboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv: 1406.1078, 2014.
    [20] Malhotra P, Ramakrishnan A, Anand G, et al. LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv: 1607.00148, 2016.
    [21] Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on International Conference on Machine Learning. Atlanta: JMLR, 2013. III-1310-III-1318.
    [22] Zhang J, Hao BW, Chen B, et al. Hierarchical reinforcement learning for course recommendation in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 435–442.
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宋晓丽,贺龙威.基于改进自编码器的在线课程推荐模型.计算机系统应用,2022,31(3):288-293

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  • 收稿日期:2021-05-07
  • 最后修改日期:2021-06-08
  • 在线发布日期: 2022-01-24
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