To provide much better recommendation service, traditional recommender systems collect a large amount of user information, which, if obtained and analyzed maliciously, can cause incalculable damage to users. Therefore...
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To provide much better recommendation service, traditional recommender systems collect a large amount of user information, which, if obtained and analyzed maliciously, can cause incalculable damage to users. Therefore, differential privacy techniques, such as noise injection, have been widely introduced into recommender systems to safeguard users' sensitive information. However, the introduction of privacy noise will lead to a degradation in recommendation quality. Hence, it is pragmatic to design a system that can furnish high quality recommendation and ensure privacy guarantee. In this article, we design a novel Differentially private recommender system with Dual semi-autoencoder recommender framework referred to as DP-DAE, which aims to improve the quality of recommendation while protecting user privacy. Specifically, DP-DAE is a hybrid framework of dual autoencoder and matrix factorization, which can effectively reduce data dimensionality to extract intricate features. In practice, to prevent potential privacy leaks, the differential privacy mechanism is incorporated into DP-DAE via introducing extra noise. Moreover, theoretical analysis certificates that DP-DAE satisfies epsilon-differential privacy. We do the experimental evaluation for DP-DAE over FilmTrust, Movielens-1M and Movielens-10M. The experimental results indicate that DP-DAE can provide privacy protection as well as high performance in recommendation tasks.
In this paper, we present a novel structure, semi-autoencoder, based on autoencoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Ex...
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ISBN:
(纸本)9783319700878;9783319700861
In this paper, we present a novel structure, semi-autoencoder, based on autoencoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.
In the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning...
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In the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning-based semi-autoencoders in conjunction with a hybrid recommendation algorithm to enhance SID tasks. Our proposed method utilizes the deep learning-based semi-autoencoder to effectively capture and learn complex patterns from high-dimensional power IoT data, facilitating the identification of anomalies indicative of potential security threats. The hybrid recommendation algorithm, which combines collaborative filtering and content-based filtering, further refines the detection process by cross-verifying the identified anomalies with historical data and contextual information, thereby improving the accuracy and reliability of the SID tasks. Through extensive simulations and practical data evaluations, our proposed framework demonstrates superior performance over conventional methods, achieving higher detection accuracy. In particular, the detection accuracy of the proposed scheme is more than 20% higher than that of the competing schemes.
The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved ...
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The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved the prediction accuracy of deep matrix factorization by incorporating the user's and/or items' auxiliary information. However, there are still two remaining drawbacks that need to be addressed. First, the initialization of latent feature representations has a substantial impact on the performance of deep matrix factorization, and most current models utilize a uniform approach to this initialization, constraining the model's optimization potential. Secondly, many existing recommendation models lack versatility and efficiency in transferring auxiliary information from users or items to expand the feature space. This paper proposes a novel model to address the issues mentioned above. By using a semi-autoencoder, the pre-trained initialization of the latent feature representation is realized in this paper. Simultaneously, this model assimilates auxiliary information, like item attributes or rating matrices from diverse domains, to generate their latent feature representations. These representations are then transferred to the target task through subspace projection distance. With this, this model can utilize auxiliary information from various sources more efficiently and this model has better versatility. This is called deep matrix factorization via feature subspace transfer. Numerical experiments on several real-world data show the improvement of this method compared with state-of-the-art methods of introducing auxiliary information about items. Compared with the deep matrix factorization model, the proposed model can achieve 6.5% improvement at most in the mean absolute error and root mean square error.
The present paper aims to propose a new neural network called sparse semi-autoencoder to overcome the vanishing information problem inherent to multi-layered neural networks. The vanishing information problem represen...
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The present paper aims to propose a new neural network called sparse semi-autoencoder to overcome the vanishing information problem inherent to multi-layered neural networks. The vanishing information problem represents a natural tendency of multi-layered neural networks to lose information in input patterns as well as training errors, including also natural reduction in information due to constraints such as sparse regularization. To overcome this problem, two methods are proposed here, namely, input information enhancement by semi-autoencoders and the separation of error minimization and sparse regularization by soft pruning. First, we try to enhance information in input patterns to prevent the information from decreasing when going through multi-layers. The information enhancement is realized in a form of new architecture called semi-autoencoders, in which information in input patterns is forced to be given to all hidden layers to keep the original information in input patterns as much as possible. Second, information reduction by the sparse regularization is separated from a process of information acquisition as error minimization. The sparse regularization is usually applied in training autoencoders, and it has a natural tendency to decrease information by restricting the information capacity. This information reduction in terms of the penalties tends to eliminate even necessary and important information, because of the existence of many parameters to harmonize the penalties with error minimization. Thus, we introduce a new method of soft pruning, where information acquisition of error minimization and information reduction of sparse regularization are separately applied without a drastic change in connection weights, as is the case of the pruning methods. The two methods of information enhancement and soft pruning try jointly to keep the original information as much as possible and particularly to keep necessary and important information by enabling the making
With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The tra...
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With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The traditional collaborative filtering utilizes matrix factorization methods to learn hidden feature representations of users and/or items. With deep learning achieved good performance in representation learning, the autoencoder model is widely applied in recommendation systems for the advantages of fast convergence and no label requirement. However, the previous recommendation systems may take the reconstruction output of an autoencoder as the prediction of missing values directly, which may deteriorate their performance and cause unsatisfactory results of recommendation. In addition, the parameters of an autoencoder need to be pre-trained ahead, which greatly increases the time complexity. To address these problems, in this paper, we propose a Hybrid Collaborative Recommendation method via Dual-autoencoder (HCRDa). More specifically, firstly, a novel dual-autoencoder is utilized to simultaneously learn the feature representations of users and items in our HCRDa, which obviously reduces time complexity. Secondly, embedding matrix factorization into the training process of the autoencoder further improves the quality of hidden features for users and items. Finally, additional attributes of users and items are utilized to alleviate the cold start problem and to make hybrid recommendations. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed method in comparison with several state-of-the-art methods.
In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most comm...
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In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful recommendation approaches, which refers to using the preferences of groups with similar interests to recommend information to other users. Recently, in addition to the traditional matrix factorization techniques, deep learning methods have been proposed to learn more abstract and higher-level representations for recommendation. However, most previous deep recommendation methods learn the higher level feature representations of users and items through an identical model structure, which ignores the different characteristics of the user-based and item-based data. In addition, the rating matrix is usually sparse which may result in a significant degradation of recommendation performance. To address these problems, we propose a representation learning method with Collaborative autoencoder for Personalized Recommendation (CAPR for short). In this method, user-based and item-based feature representations are learned by two different autoencoders for capturing different features of the data. Meanwhile, items' attributions are combined into the feature representations with semi-autoencoder for alleviating the sparsity problem. Extensive experimental results confirm the effectiveness of our proposed method compared to other state-of-the-art matrix factorization methods and deep recommendation methods.
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