Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Proactive IT Incident Prevention: Using Data Analytics to Reduce Service Interruptions

The cost of resolving user requests for IT assistance rises annually. Researchers have demonstrated that data warehouse analytic techniques can improve service, but they have not established the benefit of using global organizational data to reduce reported IT incidents. The purpose of this quantitative, quasi-experimental study was to examine the extent to which IT staff use of organizational knowledge generated from data warehouse analytical measures reduces the number of IT incidents over a 30-day period, as reported by global users of IT within an international pharmaceutical company headquartered in Germany. Organizational learning theory was used to approach the theorized relationship between organizational knowledge and user calls received. Archival data from an internal help desk ticketing system was the source of data, with access provided by the organization under study. The population for this study was all calls logged and linked to application systems registered in a configuration database, and the sample was the top 14 application systems with the highest call volume that were under the control of infrastructure management. Based on an analysis of the data using a split-plot ANOVA (SPANOVA) of Time 1, Time 2, treatment, and nontreatment data, there was a small reduction in calls in the number of reported IT incidents in the treatment group, though the reduction was not statistically significant. Implications for positive social change include reassigning employees to other tasks, rather than continuing efforts in this area, enabling employees to support alternative initiatives to drive the development of innovative therapies benefiting patients and improving employee satisfaction

Post-field ionization of Si clusters in atom probe tomography: A joint theoretical and experimental study

A major challenge for Atom Probe Tomography (APT) quantification is the inability to decouple ions which possess the same mass/charge-state ($m/n$) ratio but a different mass. For example, $^{75}{\rm{As}}^{+}$ and $^{75}{\rm{As}}{_2}^{2+}$ at ~75 Da or $^{14}{\rm{N}}^+$ and $^{28}{\rm{Si}}^{2+}$ at ~14 Da, cannot be differentiated without the additional knowledge of their kinetic energy or a significant improvement of the mass resolving power. Such mass peak overlaps lead to ambiguities in peak assignment, resulting in compositional uncertainty and an incorrect labelling of the atoms in a reconstructed volume. In the absence of a practical technology for measuring the kinetic energy of the field-evaporated ions, we propose and then explore the applicability of a post-experimental analytical approach to resolve this problem based on the fundamental process that governs the production of multiply charged molecular ions/clusters in APT, i.e., Post-Field Ionization (PFI). The ability to predict the PFI behaviour of molecular ions as a function of operating conditions could offer the first step towards resolving peak overlap and minimizing compositional uncertainty. We explore this possibility by comparing the field dependence of the charge-state-ratio for Si clusters ($\rm{Si}_2$, $\rm{Si}_3$ and $\rm{Si}_4$) with theoretical predictions using the widely accepted Kingham PFI theory. We then discuss the model parameters that may affect the quality of the fit and the possible ways in which the PFI of molecular ions in APT can be better understood. Finally, we test the transferability of the proposed approach to different material systems and outline ways forward for achieving more reliable results