329 research outputs found
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been
tremendous interests in developing RL-based recommender systems. In practical
recommendation sessions, users will sequentially access multiple scenarios,
such as the entrance pages and the item detail pages, and each scenario has its
specific characteristics. However, the majority of existing RL-based
recommender systems focus on optimizing one strategy for all scenarios or
separately optimizing each strategy, which could lead to sub-optimal overall
performance. In this paper, we study the recommendation problem with multiple
(consecutive) scenarios, i.e., whole-chain recommendations. We propose a
multi-agent RL-based approach (DeepChain), which can capture the sequential
correlation among different scenarios and jointly optimize multiple
recommendation strategies. To be specific, all recommender agents (RAs) share
the same memory of users' historical behaviors, and they work collaboratively
to maximize the overall reward of a session. Note that optimizing multiple
recommendation strategies jointly faces two challenges in the existing
model-free RL model - (i) it requires huge amounts of user behavior data, and
(ii) the distribution of reward (users' feedback) are extremely unbalanced. In
this paper, we introduce model-based RL techniques to reduce the training data
requirement and execute more accurate strategy updates. The experimental
results based on a real e-commerce platform demonstrate the effectiveness of
the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge
Managemen
The Causes and Consequences of Entrepreneurial Failure Fear: Research Framework and Future Prospects
The fear of entrepreneurial failure is the avoidance trait or negative emotion that entrepreneurs develop towards the consequences of failure. The generation of this avoidance trait or negative emotion is related to the joint interaction of factors such as the entrepreneur's cognition, achievement goals, and environment. It is an important factor that suppresses entrepreneurial willingness and behavior, and may also stimulate entrepreneurial motivation. This article reviews existing literature, analyzes the concept, influencing factors, and outcome variables of entrepreneurial failure fear, and finally proposes a research framework and future research points and directions for entrepreneurial failure fear. Research points: 1. The connotation and role of fear of entrepreneurial failure throughout the entire entrepreneurial process. 2. The fear of entrepreneurial failure has a beneficial impact on entrepreneurship. Research direction: 1. Enrich the connotation and extension of entrepreneurial failure fear, and explore the sources of failure fear in different scenarios. 2. Pay attention to the dynamic patterns of fear of entrepreneurial failure throughout the entire process of entrepreneurship. 3. Research on the mechanisms and factors that contribute to the beneficial impact of fear of entrepreneurial failure
Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models
The ability to discover abstract physical concepts and understand how they
work in the world through observing lies at the core of human intelligence. The
acquisition of this ability is based on compositionally perceiving the
environment in terms of objects and relations in an unsupervised manner. Recent
approaches learn object-centric representations and capture visually observable
concepts of objects, e.g., shape, size, and location. In this paper, we take a
step forward and try to discover and represent intrinsic physical concepts such
as mass and charge. We introduce the PHYsical Concepts Inference NEtwork
(PHYCINE), a system that infers physical concepts in different abstract levels
without supervision. The key insights underlining PHYCINE are two-fold,
commonsense knowledge emerges with prediction, and physical concepts of
different abstract levels should be reasoned in a bottom-up fashion. Empirical
evaluation demonstrates that variables inferred by our system work in
accordance with the properties of the corresponding physical concepts. We also
show that object representations containing the discovered physical concepts
variables could help achieve better performance in causal reasoning tasks,
i.e., ComPhy.Comment: Accepted to Computer Vision and Pattern Recognition (CVPR)202
An hourglass-free formulation for total Lagrangian smoothed particle hydrodynamics
The total Lagrangian smoothed particle hydrodynamics (TL-SPH) for elastic
solid dynamics suffers from hourglass modes which can grow and lead to the
failure of simulation for problems with large deformation. To address this
long-standing issue, we present an hourglass-free formulation based on
volumetric-devioatric stress decomposition. Inspired by the fact that the
artifact of nonphysical zigzag particle distribution induced by the hourglass
modes is mainly characterized by shear deformation and the standard SPH
discretization for the viscous term in the Navier-Stokes (NS) equation, the
present formulation computes the action of shear stress directly through the
Laplacian of displacement other than from the divergence of shear stress. A
comprehensive set of challenging benchmark cases are simulated to demonstrate
that, while improving accuracy and computational efficiency, the present
formulation is able to eliminate the hourglass modes and achieves very good
numerical stability with a single general effective parameter. In addition, the
deformation of a practically relevant stent structure is simulated to
demonstrate the potential of the present method in the field of biomechanics.Comment: 38 pages 21 figure
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