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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
130
Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
285
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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相关实验视频

Updated: May 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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一个多代理强化学习框架,用于跨领域的顺序推.

Huiting Liu1, Junyi Wei2, Kaiwen Zhu3

  • 1School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, Anhui, China.

Neural networks : the official journal of the International Neural Network Society
|January 29, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了跨领域顺序推的多代理强化学习框架 (MARL4CDSR). 通过智能选择和跨域传输用户数据,MARL4CDSR增强了推,优于现有方法.

关键词:
交叉注意力机制 交叉注意力机制跨领域的顺序推建议.多个代理强化学习学习多个代理强化学习学习

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 推系统是一个推系统.

背景情况:

  • 序列推模型根据历史交互预测用户行为.
  • 数据稀疏性和不同领域的用户兴趣不一致,挑战了当前的跨领域推方法.
  • 现有的方法往往忽略了协作转移策略,限制了性能.

研究的目的:

  • 为跨领域的顺序推提出一个新的多代理强化学习框架 (MARL4CDSR).
  • 为了应对数据稀疏性和域兴趣不一致的挑战,在顺序性建议中.
  • 通过优化跨领域的知识转移来提高建议准确性.

主要方法:

  • 开发了一个多代理强化学习框架 (MARL4CDSR),其中代理选择相关的源域项目进行传输.
  • 实现了一个信息融合模块,并交叉关注源域和目标域之间的项目嵌入.
  • 利用基于下一个项目得分差异的奖励函数来优化多代理系统.

主要成果:

  • 在三个亚马逊领域的评估指标中,MARL4CDSR显著超过了所有基线模型.
  • 在电影&书籍→玩具任务中,NDCG@10 (14.76%) 和HR@10 (10.25%) 显著改善,特别是在稀疏的目标领域.
  • 基于代理的项目选择和交叉注意力融合有效地提高了推性能.

结论:

  • MARL4CDSR为跨域序列推提供了强大的解决方案,有效处理数据稀疏性和域错位.
  • 多个代理商的方法使优化和协作知识的转移,导致优质的推质量.
  • 这一框架代表了利用跨领域用户数据进行个性化建议的重大进步.