Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to rep...Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.展开更多
Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficult...Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.展开更多
With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS f...With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health.This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare.More concretely,an overview is provided on three famous recommendation techniques,namely,content-based,collaborative filtering(CF)-based,and hybrid methods.Next,we provide a snapshot of five application scenarios about health RS,which are dietary recommendation,lifestyle recommendation,training recommendation,decision-making for patients and physicians,and disease-related prediction.Finally,some key challenges are given with clear justifications to this new and booming field.展开更多
Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understo...Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.展开更多
The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filte...The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filtering,but its accuracy is limited by the sparsity of the rating dataset.Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information.In collaborative filtering,selecting neighbors and determining users’similarities are the most important parts.For the selection of better neighbors,this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory.To reflect the similarity between users,a new measure that considers the effect of the number of users’common items is proposed.Specifically,the proposed novel biclustering method is first adopted to obtain local similarity and local prediction.Second,item-based collaborative filtering is used to generate global predictions.Finally,the two resultant predictions are fused to obtain a final one.Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.展开更多
Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration...Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.展开更多
User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. Howe...User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. However, traditional recommendation models do not use user authorship of items. In this paper, we show that with this additional information, we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretable topic structures for users and items. Furthermore, since user interests can be inferred from their productions, recommendations can be made for users that do not have any ratings to solve the cold-start problem.展开更多
协同过滤推荐是当前最成功的个性化推荐技术之一,但是传统的协同过滤推荐算法普遍存在推荐性能低和抗攻击能力弱的问题.针对以上问题,提出了一种基于多元化社交信任的协同过滤推荐算法CF-CRIS(collaborative filtering based on credibi...协同过滤推荐是当前最成功的个性化推荐技术之一,但是传统的协同过滤推荐算法普遍存在推荐性能低和抗攻击能力弱的问题.针对以上问题,提出了一种基于多元化社交信任的协同过滤推荐算法CF-CRIS(collaborative filtering based on credibility,reliability,intimacy and self-orientation).1)借鉴社会心理学中的信任产生原理,提出基于多个信任要素(可信度、可靠度、亲密度、自我意识导向)的信任度计算方法;2)深入研究社交网络环境中各信任要素的识别、提取和量化方法;3)基于用户间的综合信任度选取可信邻居,完成对目标用户的个性化推荐.基于通用测试数据集的实验研究结果表明:该算法不但可以极大地提高推荐系统的精确度和召回率,而且表现出良好的抗攻击能力.展开更多
基金supported by the National Natural Science Foundation of China (No. 61977003),entitled “Research on learning style for adaptive learning: modelling, identification and applications”
文摘Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.
文摘Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.
基金supported in part by the National Natural Science Foundation of China(61873148,61933007)the Royal Society of the UKthe Alexander von Humboldt Foundation of Germany。
文摘With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health.This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare.More concretely,an overview is provided on three famous recommendation techniques,namely,content-based,collaborative filtering(CF)-based,and hybrid methods.Next,we provide a snapshot of five application scenarios about health RS,which are dietary recommendation,lifestyle recommendation,training recommendation,decision-making for patients and physicians,and disease-related prediction.Finally,some key challenges are given with clear justifications to this new and booming field.
文摘Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.
基金This work was supported by Ningbo Natural Science Foundation(No.202003N4057)the National Natural Science Foundation of China(Nos.62172336 and 62032018).
文摘The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filtering,but its accuracy is limited by the sparsity of the rating dataset.Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information.In collaborative filtering,selecting neighbors and determining users’similarities are the most important parts.For the selection of better neighbors,this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory.To reflect the similarity between users,a new measure that considers the effect of the number of users’common items is proposed.Specifically,the proposed novel biclustering method is first adopted to obtain local similarity and local prediction.Second,item-based collaborative filtering is used to generate global predictions.Finally,the two resultant predictions are fused to obtain a final one.Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.
文摘Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.
基金Project supported by the Monitoring Statistics Project on Agricultural and Rural Resources,MOA,Chinathe Innovative Talents Project,MOA,Chinathe Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(No.CAAS-ASTIP-2015-AI I-02)
文摘User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. However, traditional recommendation models do not use user authorship of items. In this paper, we show that with this additional information, we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretable topic structures for users and items. Furthermore, since user interests can be inferred from their productions, recommendations can be made for users that do not have any ratings to solve the cold-start problem.
文摘协同过滤推荐是当前最成功的个性化推荐技术之一,但是传统的协同过滤推荐算法普遍存在推荐性能低和抗攻击能力弱的问题.针对以上问题,提出了一种基于多元化社交信任的协同过滤推荐算法CF-CRIS(collaborative filtering based on credibility,reliability,intimacy and self-orientation).1)借鉴社会心理学中的信任产生原理,提出基于多个信任要素(可信度、可靠度、亲密度、自我意识导向)的信任度计算方法;2)深入研究社交网络环境中各信任要素的识别、提取和量化方法;3)基于用户间的综合信任度选取可信邻居,完成对目标用户的个性化推荐.基于通用测试数据集的实验研究结果表明:该算法不但可以极大地提高推荐系统的精确度和召回率,而且表现出良好的抗攻击能力.
