Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep lear...Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.展开更多
The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means t...The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.展开更多
Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and...Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.展开更多
The collaborative filtering(CF) technique has been widely used recently in recommendation systems. It needs historical data to give predictions. However, the data sparsity problem still exists. We propose a new item-b...The collaborative filtering(CF) technique has been widely used recently in recommendation systems. It needs historical data to give predictions. However, the data sparsity problem still exists. We propose a new item-based restricted Boltzmann machine(RBM) approach for CF and use the deep multilayer RBM network structure, which alleviates the data sparsity problem and has excellent ability to extract features. Each item is treated as a single RBM, and different items share the same weights and biases. The parameters are learned layer by layer in the deep network. The batch gradient descent algorithm with minibatch is used to increase the convergence speed. The new feature vector discovered by the multilayer RBM network structure is very effective in predicting a rating and achieves a better result. Experimental results on the data set of MovieL ens show that the item-based multilayer RBM approach achieves the best performance, with a mean absolute error of 0.6424 and a root-mean-square error of 0.7843.展开更多
协同过滤推荐和基于内容的推荐是目前应用于推荐系统中的两种主流手段.传统的协同过滤模型存在着矩阵稀疏问题,基于内容的推荐又不能自动抽取深层特征,且两种推荐手段很难直接融合在一起,无法共同提升推荐系统的性能表现.充分利用了深...协同过滤推荐和基于内容的推荐是目前应用于推荐系统中的两种主流手段.传统的协同过滤模型存在着矩阵稀疏问题,基于内容的推荐又不能自动抽取深层特征,且两种推荐手段很难直接融合在一起,无法共同提升推荐系统的性能表现.充分利用了深度学习模型能够深度挖掘内容隐藏信息的特性,将栈式降噪自编码器(SDAE)运用于基于内容的推荐模型中,并将其与基于标签的协同过滤算法结合在一起,提出DLCF(Deep Learning for Collaborative Filtering)算法.经过真实数据集的验证,DLCF算法能够很大程度上克服矩阵稀疏问题,在性能上优于传统推荐算法.展开更多
基于自注意力网络和神经协同过滤模型(neural collaborative filtering,NCF)提出一种基于自注意力机制的组推荐系统模型SAGR(self-attention group recommendation),用于建模用户交互数据以及学习群组潜在偏好的表示。通过在用户级和项...基于自注意力网络和神经协同过滤模型(neural collaborative filtering,NCF)提出一种基于自注意力机制的组推荐系统模型SAGR(self-attention group recommendation),用于建模用户交互数据以及学习群组潜在偏好的表示。通过在用户级和项目级分别使用自注意力机制,动态调整组中每个用户的权重,解决偏好融合问题从而得到组表示。再通过多层神经网络框架NCF从数据中挖掘组和项目之间的交互,最终完成群组推荐。在CAMRa2011和MovieLens数据集上与同类方法进行对比,实验结果表明SAGR方法能够取得更好的组推荐结果。展开更多
文摘Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.
基金supported by the deanship of Scientific Research at Prince Sattam Bin Abdulaziz University,Alkharj,Saudi Arabia through Research Proposal No.2020/01/17215。
文摘The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.
基金supported by the Natural Science Foundation of Zhejiang Province(Nos.LQ21F020021 and LZ21F020008)Zhejiang Provincial Natural Science Foundation of China(No.LZ22F020002)the Research Start-up Project funded by Hangzhou Normal University(No.2020QD2035).
文摘Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.
基金Project supported by the National Science and Technology Suppor Plan(No.2013BAH21B02-01)the Beijing Natural Science Foundation(No.4153058)
文摘The collaborative filtering(CF) technique has been widely used recently in recommendation systems. It needs historical data to give predictions. However, the data sparsity problem still exists. We propose a new item-based restricted Boltzmann machine(RBM) approach for CF and use the deep multilayer RBM network structure, which alleviates the data sparsity problem and has excellent ability to extract features. Each item is treated as a single RBM, and different items share the same weights and biases. The parameters are learned layer by layer in the deep network. The batch gradient descent algorithm with minibatch is used to increase the convergence speed. The new feature vector discovered by the multilayer RBM network structure is very effective in predicting a rating and achieves a better result. Experimental results on the data set of MovieL ens show that the item-based multilayer RBM approach achieves the best performance, with a mean absolute error of 0.6424 and a root-mean-square error of 0.7843.
文摘协同过滤推荐和基于内容的推荐是目前应用于推荐系统中的两种主流手段.传统的协同过滤模型存在着矩阵稀疏问题,基于内容的推荐又不能自动抽取深层特征,且两种推荐手段很难直接融合在一起,无法共同提升推荐系统的性能表现.充分利用了深度学习模型能够深度挖掘内容隐藏信息的特性,将栈式降噪自编码器(SDAE)运用于基于内容的推荐模型中,并将其与基于标签的协同过滤算法结合在一起,提出DLCF(Deep Learning for Collaborative Filtering)算法.经过真实数据集的验证,DLCF算法能够很大程度上克服矩阵稀疏问题,在性能上优于传统推荐算法.
文摘基于自注意力网络和神经协同过滤模型(neural collaborative filtering,NCF)提出一种基于自注意力机制的组推荐系统模型SAGR(self-attention group recommendation),用于建模用户交互数据以及学习群组潜在偏好的表示。通过在用户级和项目级分别使用自注意力机制,动态调整组中每个用户的权重,解决偏好融合问题从而得到组表示。再通过多层神经网络框架NCF从数据中挖掘组和项目之间的交互,最终完成群组推荐。在CAMRa2011和MovieLens数据集上与同类方法进行对比,实验结果表明SAGR方法能够取得更好的组推荐结果。
