Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover what they really need. However, users’ preferences can change over time because of the age and the impact of social networks. Recommendation systems must capture users’ preferences and recommend suitable items, which is a difficult challenge. This study proposes a novel recommendation system that adapts to the changing preferences of users. By learning and adapting to users’ changing preferences better recommendations are provided. This study uses context factors as additional information to model users’ preferences more accurately. For the proposed recommendation system, the context is based on users’ most recent interaction items. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users’ preference and the importance of items. The multi-head attention module extends the diversity of users’ preferences and adapts to changing preferences. The recommendation performance is improved using additional item's temporal information to model the contextual item's representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.

This article proposes a novel asymmetric dual-regression model that combines the principles of twin-support vector machine theory with the possibilistic regression analysis. Using the principle of a twin-support vector machine, the proposed approach solves four smaller quadratic programming problems, each of which constructs the lower and upper bound functions of the possibility and necessity models, rather than a single large one. This strategy significantly reduces the time that is required for training. The output from the obtained dual-regression model is characterized by an asymmetric trapezoidal fuzzy number. The obtained asymmetric dual-regression model is more flexible and models the data distribution better than a symmetric model. The proposed approach provides a unified framework that accepts various types of crisp and fuzzy input variables by using radial kernels. The proposed dual model also indicates a degree of confidence to the predicted outputs. The explicable characteristic for the degree of confidence also means that the proposed approach is more suitable for decision-making task. The experimental results demonstrate that the proposed approach has a more efficient training procedure and better describes the inherent ambiguity in the observed phenomena.

This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair--SVR has additional advantage of using parameter for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair--SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair--SVR in both training/prediction speed and generalization ability.This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair--SVR has additional advantage of using parameter for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair--SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair--SVR in both training/prediction speed and generalization ability.

Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. The SVM also uses quadratic programming approach whose advantage in interval regression analysis is to be able to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. The proposed algorithm is a attractive approach to modeling nonlinear interval data, and is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) methodology. We apply a fuzzy membership to each input point and reformulate the optimization problem of SVM such that different input points can make different contributions to the learning of decision surface. Besides, the parameters to be identified in the SVM, such as the components within the weight vector and the bias term, are fuzzy numbers. This integration preserves the benefits of SVM learning theory and fuzzy set theory, where the SVM learning theory characterizes the properties of learning machines which enable them to effectively generalize the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system.

In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analyzed theoretically and experimentally.

Support vector machines (SVMs) have been very successful in pattern classification and function approximation problems for crisp data. In this paper, we incorporate the concept of fuzzy set theory into the support vector regression machine. The parameters to be estimated in the SVM regression, such as the components within the weight vector and the bias term, are set to be the fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model and has been attempted to treat fuzzy nonlinear regression analysis. In contrast to previous fuzzy nonlinear regression models, the proposed algorithm is a model-free method in the sense that we do not have to assume the underlying model function. By using different kernel functions, we can construct different learning machines with arbitrary types of nonlinear regression functions. Moreover, the proposed method can achieve automatic accuracy control in the fuzzy regression analysis task. The upper bound on number of errors is controlled by the user-predefined parameters. Experimental results are then presented that indicate the performance of the proposed approach.

This paper presents the pruning and model-selecting algorithms to the support vector learning for sample classification and function regression. When constructing RBF network by support vector learning we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensitivity measure and the penalty term. The kernel function parameters and the position of each support vector are updated in order to have minimal increase in error, and this makes the structure of SVM network more flexible. We illustrate this approach with synthetic data simulation and face detection problem in order to demonstrate the pruning effectiveness.

This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.

In this paper, the support vector clustering is extended to an adaptive cell growing model which maps data points to a high dimensional feature space through a desired kernel function. This generalized model is called multiple spheres support vector clustering, which essentially identifies dense regions in the original space by finding their corresponding spheres with minimal radius in the feature space. A multisphere clustering algorithm based on adaptive cluster cell growing method is developed, whereby it is possible to obtain the grade of memberships, as well as cluster prototypes in partition. The effectiveness of the proposed algorithm is demonstrated for the problem of arbitrary cluster shapes and for prototype identification in an actual application to a handwritten digit data set.