جستجوی مقالات مرتبط با کلیدواژه "matrix factorization" در نشریات گروه "فنی و مهندسی"

With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems, the users’ behavior is dynamic and their preferences change over time for different reasons. The adaptability of recommender systems to capture the evolving user preferences, which are changing constantly, is essential.Recent studies point out that the modeling and capturing the dynamics of user preferences lead to significant improvements in recommendation accuracy. In spite of the importance of this issue, only a few approaches recently proposed that take into account the dynamic behavior of the users in making recommendations. Most of these approaches are based on the matrix factorization scheme. However, most of them assume that the preference dynamics are homogeneous for all users, whereas the changes in user preferences may be individual and the time change pattern for each user differs. In addition, because the amount of numerical ratings dramatically reduced in a specific time period, the sparsity problem in these approaches is more intense. Exploiting social information such as the trust relations between users besides the users’ rating data can help to alleviate the sparsity problem. Although social information is also very sparse, especially in a time period, it is complementary to rating information. Some works use tensor factorization to capture user preference dynamics. Despite the success of these works, the processing and solving the tensor decomposition is hard and usually leads to very high computing costs in practice, especially when the tensor is large and sparse.In this paper, considering that user preferences change individually over time, and based on the intuition that social influence can affect the users’ preferences in a recommender system, a social recommender system is proposed. In this system, the users’ rating information and social trust information are jointly factorized based on a matrix factorization scheme. Based on this scheme, each users and items is characterized by a sets of features indicating latent factors of the users and items in the system. In addition, it is assumed that user preferences change smoothly, and the user preferences in the current time period depend on his/her preferences in the previous time period. Therefore, the user dynamics are modeled into this framework by learning a transition matrix of user preferences between two consecutive time periods for each individual user. The complexity analysis implies that this system can be scaled to large datasets with millions of users and items. Moreover, the experimental results on a dataset from a popular product review website, Epinions, show that the proposed system performs better than competitive methods in terms of MAE and RMSE.

DNA micro-array datasets play crucial role in machine learning and recognition of various kinds of cancer structures. Micro-array datasets are typically characterized by the high number of features and the small number of samples. Such problems may result in overfitting and low prediction accuracy of classifiers due to the irrelevant features, and therefore, they are considered as a challenging task in machine learning. The direct way to deal with such challenges is dimensionality reduction of data. In this regard, feature selection method acts as an effective solution for dimensinality reduction and increasing efficiency of learning algorithms. In this paper, by using the concept of “the basis for the DNA micro-array datasets”, a new feature selection method is introduced. To be more specific, rather than utilizing the entire micro-array dataset for tackling the problem of feature selection, a basis that is a much more smaller subset of the micro-array dataset is used. This method is based on subspace learning and matrix factorization. Finally, by making use of the DNA micro-array datasets, the effectiveness of the proposed method is evaluated, and the obtained results are compared with some state-of-the-art supervised feature selection methods.

With advance of technology, manipulation and distribution of digital media such as image, audio and video has been widespread explosively. This causes problems like unauthorized copy being more important. Watermarking is a method for copyright protection and hidden information transfer. Wavelet transform has good functionality in various watermarking methods because of its good ability of modeling human visual system. Wavelet transform extensions like basic wavelet are utilized in variety applications of image processing and good results have been reported from them. No comprehensive research has been done in efficiency of extended wavelets on watermarking robustness. In this paper efficiency of wavelet transform extensions is investigated in different attacks. For this purpose a general framework is proposed for embedding and watermark extraction based on bidiagonal singular value factorization. Results show classic wavelets have good robustness against attacks like compression and noises, but they have less robustness against geometric attacks such as rotation and shearing. Unlike classic wavelets, multidirectional wavelet extensions are robust in geometrical attacks.