The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the import...The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the importance of teaching grammar imperceptibly, but neglect to explain the rules of grammar. The diametrically isolation of implicit and explicit learning doesn＇t correspond with the English grammar teaching. So the teachers should combine the two learning styles in order to make the best use of them to teach English grammar.展开更多
Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most ...Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.展开更多
文摘The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the importance of teaching grammar imperceptibly, but neglect to explain the rules of grammar. The diametrically isolation of implicit and explicit learning doesn＇t correspond with the English grammar teaching. So the teachers should combine the two learning styles in order to make the best use of them to teach English grammar.
基金supported by the National Basic Research Program(973)of China(No.2012CB316400)the National Natural Science Foundation of China(No.61571393)
文摘Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
