753 research outputs found
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
Leveraging Demonstrations with Latent Space Priors
Demonstrations provide insight into relevant state or action space regions,
bearing great potential to boost the efficiency and practicality of
reinforcement learning agents. In this work, we propose to leverage
demonstration datasets by combining skill learning and sequence modeling.
Starting with a learned joint latent space, we separately train a generative
model of demonstration sequences and an accompanying low-level policy. The
sequence model forms a latent space prior over plausible demonstration
behaviors to accelerate learning of high-level policies. We show how to acquire
such priors from state-only motion capture demonstrations and explore several
methods for integrating them into policy learning on transfer tasks. Our
experimental results confirm that latent space priors provide significant gains
in learning speed and final performance. We benchmark our approach on a set of
challenging sparse-reward environments with a complex, simulated humanoid, and
on offline RL benchmarks for navigation and object manipulation. Videos, source
code and pre-trained models are available at the corresponding project website
at https://facebookresearch.github.io/latent-space-priors .Comment: Published in Transactions on Machine Learning Research (03/2023
MetaRec: Meta-Learning Meets Recommendation Systems
Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems.
In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches in both the meta-learning and recommendation system literature, applying them to recommendation tasks to obtain improved generalization performance.
Our proposed framework, MetaRec, includes and unifies the main state-of-the-art models in recommendation systems, extending them to be flexibly configured and efficiently operate with limited data. We empirically test the architectures created under our MetaRec framework on several recommendation benchmark datasets using a plethora of evaluation metrics and find that by taking a meta-learning approach to the collaborative filtering problem, we observe notable gains in predictive performance
SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
Uncertainty quantification is crucial to decision-making. A prominent example
is probabilistic forecasting in numerical weather prediction. The dominant
approach to representing uncertainty in weather forecasting is to generate an
ensemble of forecasts. This is done by running many physics-based simulations
under different conditions, which is a computationally costly process. We
propose to amortize the computational cost by emulating these forecasts with
deep generative diffusion models learned from historical data. The learned
models are highly scalable with respect to high-performance computing
accelerators and can sample hundreds to tens of thousands of realistic weather
forecasts at low cost. When designed to emulate operational ensemble forecasts,
the generated ones are similar to physics-based ensembles in important
statistical properties and predictive skill. When designed to correct biases
present in the operational forecasting system, the generated ensembles show
improved probabilistic forecast metrics. They are more reliable and forecast
probabilities of extreme weather events more accurately. While this work
demonstrates the utility of the methodology by focusing on weather forecasting,
the generative artificial intelligence methodology can be extended for
uncertainty quantification in climate modeling, where we believe the generation
of very large ensembles of climate projections will play an increasingly
important role in climate risk assessment.Comment: fixed a mistake of the previous version; the paper has not been
submitted to neurips 202
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
Numerous HR applications are centered around resumes and job descriptions.
While they can benefit from advancements in NLP, particularly large language
models, their real-world adoption faces challenges due to absence of
comprehensive benchmarks for various HR tasks, and lack of smaller models with
competitive capabilities. In this paper, we aim to bridge this gap by
introducing the Resume-Job Description Benchmark (RJDB). We meticulously craft
this benchmark to cater to a wide array of HR tasks, including matching and
explaining resumes to job descriptions, extracting skills and experiences from
resumes, and editing resumes. To create this benchmark, we propose to distill
domain-specific knowledge from a large language model (LLM). We rely on a
curated skill-occupation graph to ensure diversity and provide context for LLMs
generation. Our benchmark includes over 50 thousand triples of job
descriptions, matched resumes and unmatched resumes. Using RJDB, we train
multiple smaller student models. Our experiments reveal that the student models
achieve near/better performance than the teacher model (GPT-4), affirming the
effectiveness of the benchmark. Additionally, we explore the utility of RJDB on
out-of-distribution data for skill extraction and resume-job description
matching, in zero-shot and weak supervision manner. We release our datasets and
code to foster further research and industry applications
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
The egocentric and exocentric viewpoints of a human activity look
dramatically different, yet invariant representations to link them are
essential for many potential applications in robotics and augmented reality.
Prior work is limited to learning view-invariant features from paired
synchronized viewpoints. We relax that strong data assumption and propose to
learn fine-grained action features that are invariant to the viewpoints by
aligning egocentric and exocentric videos in time, even when not captured
simultaneously or in the same environment. To this end, we propose AE2, a
self-supervised embedding approach with two key designs: (1) an object-centric
encoder that explicitly focuses on regions corresponding to hands and active
objects; (2) a contrastive-based alignment objective that leverages temporally
reversed frames as negative samples. For evaluation, we establish a benchmark
for fine-grained video understanding in the ego-exo context, comprising four
datasets -- including an ego tennis forehand dataset we collected, along with
dense per-frame labels we annotated for each dataset. On the four datasets, our
AE2 method strongly outperforms prior work in a variety of fine-grained
downstream tasks, both in regular and cross-view settings.Comment: Project website: https://vision.cs.utexas.edu/projects/AlignEgoExo
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