211 research outputs found
Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
Differential Privacy has become a widely popular method for data protection
in machine learning, especially since it allows formulating strict mathematical
privacy guarantees. This survey provides an overview of the state-of-the-art of
differentially private centralized deep learning, thorough analyses of recent
advances and open problems, as well as a discussion of potential future
developments in the field. Based on a systematic literature review, the
following topics are addressed: auditing and evaluation methods for private
models, improvements of privacy-utility trade-offs, protection against a broad
range of threats and attacks, differentially private generative models, and
emerging application domains.Comment: 35 pages, 2 figure
Impression-Aware Recommender Systems
Novel data sources bring new opportunities to improve the quality of
recommender systems. Impressions are a novel data source containing past
recommendations (shown items) and traditional interactions. Researchers may use
impressions to refine user preferences and overcome the current limitations in
recommender systems research. The relevance and interest of impressions have
increased over the years; hence, the need for a review of relevant work on this
type of recommenders. We present a systematic literature review on recommender
systems using impressions, focusing on three fundamental angles in research:
recommenders, datasets, and evaluation methodologies. We provide three
categorizations of papers describing recommenders using impressions, present
each reviewed paper in detail, describe datasets with impressions, and analyze
the existing evaluation methodologies. Lastly, we present open questions and
future directions of interest, highlighting aspects missing in the literature
that can be addressed in future works.Comment: 34 pages, 103 references, 6 tables, 2 figures, ACM UNDER REVIE
Predicting Academic Performance: A Systematic Literature Review
The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe
BARS: Towards Open Benchmarking for Recommender Systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite significant progress made in both research
and practice of recommender systems, to date, there is a lack of a
widely-recognized benchmarking standard in this field. Many existing studies
perform model evaluations and comparisons in an ad-hoc manner, for example, by
employing their own private data splits or using different experimental
settings. Such conventions not only increase the difficulty in reproducing
existing studies, but also lead to inconsistent experimental results among
them. This largely limits the credibility and practical value of research
results in this field. To tackle these issues, we present an initiative project
(namely BARS) aiming for open benchmarking for recommender systems. In
comparison to some earlier attempts towards this goal, we take a further step
by setting up a standardized benchmarking pipeline for reproducible research,
which integrates all the details about datasets, source code, hyper-parameter
settings, running logs, and evaluation results. The benchmark is designed with
comprehensiveness and sustainability in mind. It covers both matching and
ranking tasks, and also enables researchers to easily follow and contribute to
the research in this field. This project will not only reduce the redundant
efforts of researchers to re-implement or re-run existing baselines, but also
drive more solid and reproducible research on recommender systems. We would
like to call upon everyone to use the BARS benchmark for future evaluation, and
contribute to the project through the portal at:
https://openbenchmark.github.io/BARS.Comment: Accepted by SIGIR 2022. Note that version v5 is updated to keep
consistency with the ACM camera-ready versio
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