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 th...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(ITMF)models.Experimental results proved the effectiveness of the methods.展开更多
In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution o...In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.展开更多
协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两...协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。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
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(ITMF)models.Experimental results proved the effectiveness of the methods.
基金supported by the National KeyResearch and Development Program of China(2018AAA0103203)the National Natural Science Foundation of China(62073036,62076031)the Beijing Natural Science Foundation(4202071)。
文摘In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.
文摘协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。
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.
