Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships

Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated\ud by Collaborative Filtering Systems (CFSs). Traditional CFSs\ud based on Matrix Factorization operate on the ratings provided\ud by users and have been recently extended to incorporate\ud demographic aspects such as age and gender. In this paper we\ud propose to merge CF techniques based on Matrix Factorization\ud and information regarding social friendships in order to\ud provide users with more accurate suggestions and rankings\ud on items of their interest. The proposed approach has been\ud evaluated on a real-life online social network; the experimental\ud results show an improvement against existing CF approaches.\ud A detailed comparison with related literature is also presen

Monitoring Conceptual Development: Design Considerations of a Formative Feedback tool

This paper presents the design considerations of a tool aiming at providing formative feedback. The tool uses Latent Semantic Analysis, to automatically generate reference models and provide learners a means to compare their conceptual development against these models. The design of the tool considers a theoretical background which combines research on expertise development, knowledge creation, and conceptual development assessment. The paper also illustrates how the tool will work using a problem and solution scenario, and presents initial validations results. Finally the paper draws conclusions and future work

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc