Управление финансовым состоянием предприятия (на примере СП ОАО «Спартак»)

The purpose of this paper is to explore the characteristics of information systems (IS) maintenance within an IT and organizational setting. We discuss the characteristics of maintenance objects’ focus and content. Our results are based on qualitative case studies. In this paper a case study of a Swedish Bank is used to illustrate our discussion. Our findings show that maintenance objects can be defined by processes and/or functions or products and/or services within an organizational setting. This is done in order to increase a business perspective in maintenance management and to clarify roles of responsibility for organizational changes required from new IT capabilities. According to our findings maintenance objects can contain business solutions and IT solutions. This implies that business beneficial maintenance is supported by close cooperation between actors from the organizational setting and the IT organization. The result of the paper is a characterization of IS maintenance through definition of maintenance objects’ focus and content

Predicting the Performance of Recommender Systems: An Information Theoretic Approach

Proceedings of Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23318-0_5Performance prediction is an appealing problem in Recommender Systems, as it enables an array of strategies for deciding when to deliver or hold back recommendations based on their foreseen accuracy. The problem, however, has been barely addressed explicitly in the area. In this paper, we propose adaptations of query clarity techniques from ad-hoc Information Retrieval to define performance predictors in the context of Recommender Systems, which we refer to as user clarity. Our experiments show positive results with different user clarity models in terms of the correlation with single recommender’s performance. Empiric results show significant dependency between this correlation and the recommendation method at hand, as well as competitive results in terms of average correlation.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), University Autónoma de Madrid and the Community of Madrid (CCG10-UAM/TIC-5877

Predicting Neighbor Goodness in Collaborative Filtering

Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the subarea of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to define predictors of neighbor performance in CF. The proposed predictors are introduced in a memory-based CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The approach is tested with encouraging empirical results, as the dynamic variants consistently outperform the baseline algorithms, with increasing difference on small neighborhoods