275 research outputs found
Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper conducts a comprehensive analysis of the evolution and
contemporary landscape of recommendation systems, which have been extensively
incorporated across a myriad of web applications. It delves into the
progression of personalized recommendation methodologies tailored for online
products or services, organizing the array of recommendation techniques into
four main categories: content-based, collaborative filtering, knowledge-based,
and hybrid approaches, each designed to cater to specific contexts. The
document provides an in-depth review of both the historical underpinnings and
the cutting-edge innovations in the domain of recommendation systems, with a
special focus on implementations leveraging big data analytics. The paper also
highlights the utilization of prominent datasets such as MovieLens, Amazon
Reviews, Netflix Prize, Last.fm, and Yelp in evaluating recommendation
algorithms. It further outlines and explores the predominant challenges
encountered in the current generation of recommendation systems, including
issues related to data sparsity, scalability, and the imperative for
diversified recommendation outputs. The survey underscores these challenges as
promising directions for subsequent research endeavors within the discipline.
Additionally, the paper examines various real-life applications driven by
recommendation systems, addressing the hurdles involved in seamlessly
integrating these systems into everyday life. Ultimately, the survey
underscores how the advancements in recommendation systems, propelled by big
data technologies, have the potential to significantly enhance real-world
experiences.Comment: 34 pages, 8 figures, 2 tabl
Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows
Gravitational wave (GW) astronomy is witnessing a transformative shift from
terrestrial to space-based detection, with missions like Taiji at the
forefront. While the transition brings unprecedented opportunities for
exploring massive black hole binaries (MBHBs), it also imposes complex
challenges in data analysis, particularly in parameter estimation amidst
confusion noise. Addressing this gap, we utilize scalable normalizing flow
models to achieve rapid and accurate inference within the Taiji environment.
Innovatively, our approach simplifies the data's complexity, employs a
transformation mapping to overcome the year-period time-dependent response
function, and unveils additional multimodality in the arrival time parameter.
Our method estimates MBHBs several orders of magnitude faster than conventional
techniques, maintaining high accuracy even in complex backgrounds. These
findings significantly enhance the efficiency of GW data analysis, paving the
way for rapid detection and alerting systems and enriching our ability to
explore the universe through space-based GW observation.Comment: 14 pages, 7 figures. Published versio
- …