Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Best practices for abandoned, lost or otherwise discarded fishing gear (aldfg) in Africa

Papers presented virtually at the 41st International Southern African Transport Conference on 10-13 July 2070Sustainable Seas Trust (SST) is a non-profit organisation based in Gqeberha, South Africa, with a vision where the people of Africa and their seas flourish together. One of SST’s projects focuses on sea-based sources of waste with the aim of reducing all plastic waste (both garbage and fishing gear) that originates from the fishing and maritime sectors. Garbage and fishing gear impacts on maritime transport as it can become entangled in ship rudders, thereby impeding movement, and is costly to remove. Activities of the project include fishing gear waste surveys to determine the presence of gear waste in the environment, engagements with recreational and commercial fishers, and collaboration with port waste management authorities, to develop interventions aimed to reduce fishing and maritime waste. Recommendations to reduce, prevent, and mitigate abandoned, lost or discarded fishing gear in the environment are already developed globally. The project is currently developing a guide that takes the global best practice recommendations and applies them to suit African countries. A draft of the best practice recommendations will be presented

Mental Health Carve-Outs: Effects and Implications

To control the rise in expenditures and to increase access to mental health and substance abuse (MH/SA) services, a growing number of employers and states are implementing a “carve-out.” Under this arrangement, the sponsor separates insurance benefits by disease or condition, service category, or population and contracts separately for the management of care and/or associated risks. A carve-out allows a unique set of managed care techniques to be applied to a subset of particularly costly or complex benefits. This article describes various carve-out models, discusses the potential advantages and disadvantages of a full carve-out, and summarizes recent public and private sector research regarding the strategy’s effects on access and use, cost savings and shifting, and quality of care. It concludes by discussing approaches to the assessment and monitoring of the processes and outcomes associated with a MH/SA carve-out.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68797/2/10.1177_107755879905600203.pd

Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as Automated Rendezvous and Docking, (AR&D).) The crewed versions of the spacecraft may also perform AR&D, possibly with a different level of automation and/or autonomy, and must also provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the CEV requirements. The relatively low technology readiness of relative navigation sensors for AR&D has been carried as one of the CEV Projects top risks. The AR&D Sensor Technology Project seeks to reduce this risk by increasing technology maturation of selected relative navigation sensor technologies through testing and simulation, and to allow the CEV Project to assess the relative navigation sensors

Higher education analytics: New trends in program assessments

© 2018, Springer International Publishing AG, part of Springer Nature. End of course evaluations technologies can provide critical analytics that can be used to improve the academic outcomes of almost any university. This paper presents key findings from a study conducted on more than twenty different academic degree-programs, regarding their use of end of course evaluation technology. Data was collected from an online survey instrument, in-depth interviews with academic administrators, and two case studies, one in the US and another in the UAE. The study reveals new trends including sectioning and categorization; questions standardization and benchmarking; alignment with key performance indicators and key learning outcomes; and grouping by course, program outcome, program, college, etc. in addition to those vertical structures, higher education institutions are vertically examining a specific question(s) across

Promotion of rapid testing for HIV in primary care (RHIVA2): a cluster-randomised controlled trial

Department of Health, NHS City and Hackney; (ClinicalTrials.gov: ISRCTN63473710)

Air pollution, ethnicity and telomere length in east London schoolchildren: An observational study

This study was funded/supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London, Dr. and Mrs. Lee Iu Cheung Fund, and Hackney Primary Care Trust (PCT)