Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

American Geriatrics Society and National Institute on Aging Bench-to-Bedside conference: sensory impairment and cognitive decline in older adults

This article summarizes the presentations and recommendations of the tenth annual American Geriatrics Society and National Institute on Aging Bench‐to‐Bedside research conference, “Sensory Impairment and Cognitive Decline,” on October 2–3, 2017, in Bethesda, Maryland. The risk of impairment in hearing, vision, and other senses increases with age, and almost 15% of individuals aged 70 and older have dementia. As the number of older adults increases, sensory and cognitive impairments will affect a growing proportion of the population. To limit its scope, this conference focused on sensory impairments affecting vision and hearing. Comorbid vision, hearing, and cognitive impairments in older adults are more common than would be expected by chance alone, suggesting that some common mechanisms might affect these neurological systems. This workshop explored the mechanisms and consequences of comorbid vision, hearing, and cognitive impairment in older adults; effects of sensory loss on the aging brain; and bench‐to‐bedside innovations and research opportunities. Presenters and participants identified many research gaps and questions; the top priorities fell into 3 themes: mechanisms, measurement, and interventions. The workshop delineated specific research questions that provide opportunities to improve outcomes in this growing population.Funding was provided by National Institutes of Health (NIH) Grant U13 AG054139-01. Dr. Whitson's efforts and contributions were supported by R01AG043438, R24AG045050, UH2AG056925, and 5P30AG028716. Dr. Lin's effort and contributions were also supported by R01AG055426, R01HL096812, and R33DC015062. (U13 AG054139-01 - National Institutes of Health (NIH); R01AG043438; R24AG045050; UH2AG056925; 5P30AG028716; R01AG055426; R01HL096812; R33DC015062)Accepted manuscrip

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of CSEDU 2020 by SciTePres