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Attenuated and normalized item-item product network for sequential recommendation.

Weiqiang Di1, Zhihao Wu1, Youfang Lin1

  • 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

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|February 17, 2022
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Summary
This summary is machine-generated.

This study introduces an improved sequential recommendation model. It enhances item-item relevance by considering the decaying influence of user behavior over time, leading to better recommendations.

Keywords:
Item co-occurrenceItem-item productRecommendationSequential recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Sequential recommendation systems leverage user behavior history for accurate predictions.
  • Item-item Product (IIP) models capture pairwise item relationships but often overlook temporal dynamics.
  • Traditional IIP methods can be suboptimal as they don't account for the varying influence of historical user interactions.

Purpose of the Study:

  • To propose an enhanced Item-item Product (IIP) mechanism for sequential recommendation.
  • To address the limitations of existing IIP models by incorporating position-awareness and decaying influence.
  • To improve the model's ability to identify user preferences, especially in sparse data scenarios.

Main Methods:

  • Developed an attenuated IIP mechanism that applies an exponential decay to historical item influence based on position.
  • Introduced a complementary normalized IIP mechanism to handle non-monotonous influence trends.
  • Enhanced discrimination of user favorite items by adjusting the matching degree gap.

Main Results:

  • The proposed model demonstrated superior performance compared to state-of-the-art sequential recommendation methods.
  • Experiments on five real-world datasets validated the effectiveness of the enhanced IIP approach.
  • The position-aware and normalized IIP mechanisms significantly improved recommendation accuracy.

Conclusions:

  • The proposed attenuated and normalized IIP mechanisms offer a more effective approach to sequential recommendation.
  • Accounting for the temporal decay and non-monotonous influence of user behavior is crucial for accurate recommendations.
  • The model shows promise for improving user experience in e-commerce and content platforms.