486 research outputs found
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Combinatorial features are essential for the success of many commercial
models. Manually crafting these features usually comes with high cost due to
the variety, volume and velocity of raw data in web-scale systems.
Factorization based models, which measure interactions in terms of vector
product, can learn patterns of combinatorial features automatically and
generalize to unseen features as well. With the great success of deep neural
networks (DNNs) in various fields, recently researchers have proposed several
DNN-based factorization model to learn both low- and high-order feature
interactions. Despite the powerful ability of learning an arbitrary function
from data, plain DNNs generate feature interactions implicitly and at the
bit-wise level. In this paper, we propose a novel Compressed Interaction
Network (CIN), which aims to generate feature interactions in an explicit
fashion and at the vector-wise level. We show that the CIN share some
functionalities with convolutional neural networks (CNNs) and recurrent neural
networks (RNNs). We further combine a CIN and a classical DNN into one unified
model, and named this new model eXtreme Deep Factorization Machine (xDeepFM).
On one hand, the xDeepFM is able to learn certain bounded-degree feature
interactions explicitly; on the other hand, it can learn arbitrary low- and
high-order feature interactions implicitly. We conduct comprehensive
experiments on three real-world datasets. Our results demonstrate that xDeepFM
outperforms state-of-the-art models. We have released the source code of
xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.Comment: 10 page
From Anecdotal Evidence to Quantitative Evaluation Methods:A Systematic Review on Evaluating Explainable AI
The rising popularity of explainable artificial intelligence (XAI) to
understand high-performing black boxes, also raised the question of how to
evaluate explanations of machine learning (ML) models. While interpretability
and explainability are often presented as a subjectively validated binary
property, we consider it a multi-faceted concept. We identify 12 conceptual
properties, such as Compactness and Correctness, that should be evaluated for
comprehensively assessing the quality of an explanation. Our so-called Co-12
properties serve as categorization scheme for systematically reviewing the
evaluation practice of more than 300 papers published in the last 7 years at
major AI and ML conferences that introduce an XAI method. We find that 1 in 3
papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate
with users. We also contribute to the call for objective, quantifiable
evaluation methods by presenting an extensive overview of quantitative XAI
evaluation methods. This systematic collection of evaluation methods provides
researchers and practitioners with concrete tools to thoroughly validate,
benchmark and compare new and existing XAI methods. This also opens up
opportunities to include quantitative metrics as optimization criteria during
model training in order to optimize for accuracy and interpretability
simultaneously.Comment: Link to website added: https://utwente-dmb.github.io/xai-papers
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong
dependence on the spatiotemporal information, i.e., in different geographical
locations and at different times, user click behavior will change
significantly. Appropriate spatiotemporal enhancement modeling of user click
behavior and large-scale sparse attributes is key to building an LBS model.
Although most of existing methods have been proved to be effective, they are
difficult to apply to takeaway scenarios due to insufficient modeling of
spatiotemporal information. In this paper, we address this challenge by seeking
to explicitly model the timing and locations of interactions and proposing a
Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a
Spatiotemporal Profile Activation module to capture common spatiotemporal
preference through attribute features. A Spatiotemporal Preference Activation
is further applied to model the personalized spatiotemporal preference embodied
by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention
mechanism is adopted to generate different parameters for target attention at
different locations and times, thereby improving the personalized
spatiotemporal awareness of the model.Comprehensive experiments are conducted
on three large-scale industrial datasets, and the results demonstrate the
state-of-the-art performance of our methods. In addition, we have also released
an industrial dataset for takeaway industry to make up for the lack of public
datasets in this community.Comment: accepted by CIKM workshop 202
The U.S. Census Bureau Adopts Differential Privacy
The U.S. Census Bureau announced, via its Scientific Advisory Committee, that it would protect the publications of the 2018 End-to-End Census Test (E2E) using differential privacy. The E2E test is a dress rehearsal for the 2020 Census, the constitutionally mandated enumeration of the population used to reapportion the House of Representatives and redraw every legislative district in the country. Systems that perform successfully in the E2E test are then used in the production of the 2020 Census. Motivation: The Census Bureau conducted internal research that confirmed that the statistical disclosure limitation systems used for the 2000 and 2010 Censuses had serious vulnerabilities that were exposed by the Dinur and Nissim (2003) database reconstruction theorem. We designed a differentially private publication system that directly addressed these vulnerabilities while preserving the fitness for use of the core statistical products. Problem statement: Designing and engineering production differential privacy systems requires two primary components: (1) inventing and constructing algorithms that deliver maximum accuracy for a given privacy-loss budget and (2) insuring that the privacy-loss budget can be directly controlled by the policy-makers who must choose an appropriate point on the accuracy-privacy-loss tradeoff. The first problem lies in the domain of computer science. The second lies in the domain of economics. Approach: The algorithms under development for the 2020 Census focus on the data used to draw legislative districts and to enforce the 1965 Voting Rights Act (VRA). These algorithms efficiently distribute the noise injected by differential privacy. The Data Stewardship Executive Policy Committee selects the privacy-loss parameter after reviewing accuracy-privacy-loss graphs
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