Goal-based hybrid filtering for user-to-user Personalized Recommendation

Recommendation systems are gaining great importance with e-Learning and multimedia on the internet. It fails in some situations such as new-user profile (cold-start) issue. To overcome this issue, we propose a novel goalbased hybrid approach for user-to-user personalized similarity recommendation and present its performance accuracy. This work also helps to improve collaborative filtering using k-nearest neighbor as neighborhood collaborative filtering (NCF) and content-based filtering as content-based collaborative filtering (CBCF). The purpose of combining k-nn with recommendation approaches is to increase the relevant recommendation accuracy and decrease the new-user profile (cold-start) issue. The proposed goal-based approach associated with nearest neighbors, compare personalized profile preferences and get the similarities between users. The paper discussed research architecture, working of proposed goal-based approach, its experimental steps and initial results.DOI:http://dx.doi.org/10.11591/ijece.v3i3.241

Goal-based framework for multi-user personalized similaritiesin e-learning scenarios

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively

E-Learning recommender systems based on goal-based hybrid filtering

This research work is based on the thesis contribution by proposing the goal-based hybrid filtering approach in e-learning recommender systems (eLearningRecSys). The proposed work has been used to analyze the personalized similarities between learner's profile preferences collaboratively. The proposed work consists of two hybridizations: the first hybridization has been made with content-based filtering and collaborative features to overcome the new-learners zero-rated profile recommendations issue; the second hybridization has been done with collaborative filtering and k-neighborhood scheme features to improve the average-learner's low-rated profile recommendations issue. Therefore, the proposed goal-based hybrid filtering approach that hybridized content-based filtering, collaborative filtering and k-neighborhood features simultaneously works on both types of learner's profiles recommendation issues in e-learning environments. The experiments in the proposed work are done using the famous “MovieLens” dataset, while the evaluation of experimental results has been performed with mean of precision 83.44% and mean of recall 85.22%, respectively. t-test result shows the probability difference value of 0.29 between the proposed hybrid approach and the evaluated literature work. The results demonstrate the effectiveness of the proposed hybrid recommender systems in e-learning scenarios

Secure dynamic system development method (SDSDM): a survey about its suitability

Building secure software is about taking security into account during all phases of software development. However, the major problem in agile methods is the lack of basic security elements in their phases and practices. One of such method is Dynamic System Development Method (DSDM).Based on this study, we have observed that the original/traditional DSDM does not help guide the agile to develop secure software. In order to address this issue, we introduced additional phases and sub-phases to the original/traditional DSDM to integrate security. The proposed model is named Secure Dynamic System Development Method (SDSDM), which has six phases. These phases and sub-phases are feasibility study, functional model iteration, secure functional model iteration, secure design, design and build iteration and implementation. Our findings highlight an improved agility in DSDM after integration of security. However, the study focuses on a questionnaire (survey) where the subject matter experts’ opinion has been used to validate our model. Based on the experts’ opinion, we can say that it is possible to develop secure software using SDSDM model without affecting its agility negatively

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health