No abstract

Introduction. Recommender systems are extensively deployed to provide online users with advisory services, and the design of recommender systems functional features has received substantial attention in academic studies. The social aspects of human–recommender systems interactions, however, have been less explored. Furthermore, measuring user satisfaction, though natural in a business environment, is often challenging for recommender systems research. Thus, it is assumed that the information system success model can be adapted to the system success measurement in recommender systems. Method. This study provides the first empirical test of an adaptation of the information system success model in the context of recommender systems. Additionally, the dual-coding theory argument was introduced to examine the model. Analysis. Based on the proposed model, two presentation types are compared. An experimental design is used and a questionnaire is developed to analysis. Results. The experiment's results show that the recommender system designed on the basis of the dual-coding theory is better than the traditional recommender system on all aspects of the facet. Conclusions. Perceived usefulness and the user satisfaction have a significant positive relationship both with continued intention to use and continued intention to provide.

A current-based security-constrained optimal power flow (SCOPF) is presented in this paper and an Enhanced Particle Swarm Pptimization (EPSO) is developed to solve the non-convex optimal power flow problem. The SCOPF can be divided into three steps involving security analysis, severest event selection, and a preventive algorithm. Firstly, novel security analysis is conducted before a fault occurs in the system using the current-based power flow technique. Secondly, a ranking method is used to highlight the most severe events caused by a specific facility. Finally, a preventive algorithm makes use of the contingency information, and can maintain the operator system security, avoiding congestion when a fault occurs. In addition, this method not only enhances the neighborhood search, but also searches for the optimum solution quickly to advance the convergence.

Introduction. Recommender systems are extensively deployed to provide online users with advisory services, and the design of recommender systems' functional features has received substantial research attention. The social aspects of such system interactions, however, have been less explored. Furthermore, measuring user satisfaction, though natural in a business environment, is often challenging for recommender systems research. Method. This study provides the first empirical test of an adaptation of the information system success model in the context of recommender systems. Additionally, the dual-coding theory argument was introduced to examine the model. Analysis. Based on the proposed model, two presentation types are compared. An experimental design is used and a questionnaire is developed for analysis. Results. The experiment's results have shown that the recommender system designed on the basis of the dual-coding theory is better than the traditional recommender system in all aspects. Conclusions. Perceived usefulness and the user satisfaction have a significant positive relationship both with continued intention to use and continued intention to provide.