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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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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...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Social Cognitive Perspective on Personality01:30

Social Cognitive Perspective on Personality

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Social cognitive perspectives on personality emphasize the importance of conscious awareness, beliefs, expectations, and goals in shaping behavior. These perspectives incorporate behaviorist principles, such as learning through reinforcement and conditioning, but extend beyond them by highlighting human reasoning and planning. Unlike traditional behaviorist views, social cognitive theory focuses on how individuals reflect on their past experiences and plan for future outcomes by considering...
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Beyond single perspective bias: Fusing personalized and common preferences for comprehensive personal preference

JiaXin Wu1, Guangxiong Chen1, Chenglong Pang2

  • 1Department of Data Science and Business Intelligence, School of Management, Guangdong University of Technology, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
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Summary

This study introduces P&CGCN, a novel recommendation system that uniquely fuses personalized and common user preferences. It improves accuracy by adaptively balancing these preferences, outperforming existing models.

Keywords:
Common preferenceGCNPersonalized preferencePopularityRecommendation system

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Data Science

Background:

  • Graph Convolutional Network (GCN)-based recommendation systems often exhibit bias towards popular items, neglecting personalized user preferences.
  • Existing methods that suppress popular item information may overlook valuable common preference signals, leading to another form of bias.

Purpose of the Study:

  • To propose P&CGCN, a unified framework that collaboratively fuses personalized and common user preferences in recommendation systems.
  • To address the limitations of existing GCN models by avoiding single-perspective biases.

Main Methods:

  • Developed a unified framework with intra-layer aggregation and inter-layer combination.
  • Introduced P&C degree for adaptive quantification of personal and common preferences within layers.
  • Designed P&C depth to prioritize shallow personalized and deep common preference signals across layers.

Main Results:

  • P&CGCN demonstrates superior performance and efficiency compared to existing methods on four real-world datasets.
  • Achieved approximately 20% performance improvement over LightGCN on sparse, large-scale datasets.
  • Showcased at least a 2x speedup in training efficiency.

Conclusions:

  • P&CGCN effectively balances personalized and common preferences, leading to more accurate and efficient recommendations.
  • The proposed P&C degree and P&C depth mechanisms are key to mitigating bias in GCN-based recommenders.
  • This approach offers a significant advancement for recommendation systems, particularly in handling data sparsity.