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