基于特征迁移的在线教育导师推荐方法
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摘要
在推荐系统中,数据稀疏和数据冷启动问题一直是待解决的重要难题。针对推荐系统中用户数量过少、评价数据稀疏、模型启动困难等问题,以及针对在线教育领域存在特征关联的特性,提出了一种全新的基于特征迁移的导师推荐方法。为了迁移出更多有用的信息,该方法基于有限的领域特征,在目标领域和训练领域之间建立了一个基于特征相似度的桥梁。首先,获取训练领域和目标领域的推荐矩阵。然后,向量化用户和项目的特征空间,计算目标领域和训练领域之间特征的相似度。最后,构建特征迁移模型对目标领域进行迁移,得出目标推荐矩阵。研究结果表明,提出的方法能够很好地解决在线教育导师推荐领域中数据冷启动以及数据稀疏问题,与传统的推荐方法相比取得了很好的效果。
In recommendation system research,the problem of data sparseness and cold start has always been an important problem to be solved. Faced with the challenges that the number of users in e-learning systems was too relatively small,the evaluation data was sparse,and the model was difficult to start,a novel feature transfer-based recommendation method was proposed in feature-associated e-learning systems. In order to transfer more useful information,the method was designed to establish a bridge based on feature similarity between the target domain and the training domain via limited domain characteristics. First,the method obtained a recommendation matrix for the training and target domains. Then,the feature space of the user and the item was vectorized,and the similarity of features between the target domain and the training domain was calculated. Finally,the feature transfer model was constructed to migrate the target domain and the target recommendation matrix was proposed. The experimental results show that the proposed method can solve the cold start and data sparse problems in e-learning tutor recommendation task,and has achieved good results compared with the traditional recommendation methods.
In recommendation system research,the problem of data sparseness and cold start has always been an important problem to be solved. Faced with the challenges that the number of users in e-learning systems was too relatively small,the evaluation data was sparse,and the model was difficult to start,a novel feature transfer-based recommendation method was proposed in feature-associated e-learning systems. In order to transfer more useful information,the method was designed to establish a bridge based on feature similarity between the target domain and the training domain via limited domain characteristics. First,the method obtained a recommendation matrix for the training and target domains. Then,the feature space of the user and the item was vectorized,and the similarity of features between the target domain and the training domain was calculated. Finally,the feature transfer model was constructed to migrate the target domain and the target recommendation matrix was proposed. The experimental results show that the proposed method can solve the cold start and data sparse problems in e-learning tutor recommendation task,and has achieved good results compared with the traditional recommendation methods.
引文
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2 Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge&Data Engineering,2005,17(6):734-749
3 Yu K,Schwaighofer A,Tresp V,et al.Probabilistic memory-based collaborative filtering[J].IEEE Transactions on Knowledge and Data Engineering,2004,16(1):56-69
4 Sun M,Li F,Lee J,et al.Learning multiple-question decision trees for cold-start recommendation[C]//Proceedings of the Sixth ACMInternational Conference on Web Search and Data Mining.New York:ACM,2013:445-454
5 Zhou K,Yang S H,Zha H.Functional matrix factorizations for coldstart recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2011:315-324
6 Pan S J,Yang Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359
7 Bickel S,Scheffer T.Discriminative learning for differing training and test distributions[C]//Proceedings of the Twenty-fourth International Conference on Machine Learning.New York:ACM,2007:81-88
8 Dai W,Yang Q,Xue G R,et al.Boosting for transfer learning[C]//Proceedings of the Twenty-fourth International Conference on Machine Learning.New York:ACM,2007:193-200
9 Quattoni A,Collins M,Darrell T.Transfer learning for image classification with sparse prototype representations[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2008:1-8
10 Raina R,Ng A Y,Koller D.Constructing informative priors using transfer learning[C]//Proceedings of the 23rd International Conference on Machine Learning.New York:ACM,2006:713-720
11 Lee H,Battle A,Raina R,et al.Efficient sparse coding algorithms[C]//Proceedings of Annual Conference on Neural Information Processing Systems.Cambridge:MIT Press,2006:801-808
12 Deng J,Zhang Z,Marchi E,et al.Sparse autoencoder-based feature transfer learning for speech emotion recognition[C]//Affective Computing and Intelligent Interaction.New York:IEEE,2013:511-516
13 Stark M,Goesele M,Schiele B.A shape-based object class model for knowledge transfer[C]//Proceedings of IEEE International Conference on Computer Vision.New York:IEEE,2009:373-380
14 Bonilla E V,Chai K M A,Williams C K I.Multi-task gaussian process prediction[C]//Proceedings of Annual Conference on Neural Information Processing Systems.Cambridge:MIT Press,2007:153-160
15 Lawrence N D,Platt J C.Learning to learn with the informative vector machine[C]//International Conference on Machine Learning.New York:ACM,2004:65
16唐久红.迁移学习在垂直电商推荐系统上的应用[D].南京:南京大学,2013Tang Jiuhong.Submitted in partial fulfillment of the requirements for the degree of master of engineering[D].Nanjing:Nanjing University,2013
17柯良文.基于迁移学习的协同过滤推荐算法研究[D].泉州:华侨大学,2014Ke Liangwen.A survey of collaborative filtering based on transfer learning[D].Quanzhou:Huaqiao University,2014
18 Li B,Yang Q,Xue X.Can movies and books Collaborate?crossdomain collaborative filtering for sparsity reduction[C]//Proceedings of the International Joint Conference on Artificial Intelligence.Burlington:Morgan Kaufmann Publishers,2009:2052-2057
19 Pan W,Xiang E W,Liu N N,et al.Transfer learning in collaborative filtering for sparsity reduction[C]//Proceedings of the TwentySixth AAAI Conference on Artificial Intelligence.Palo Alto:AAAIPress,2010:662-668
20 Ding C,Li T,Peng W,et al.Orthogonal nonnegative matrix t-factorizations for clustering[C]//Proceedings of the 12th ACM SIGK-DD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2006:126-135