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Personalized Item Recommendation Algorithm for Outdoor Sports.

Hao Lei1, Xinru Shan2, Liwei Jiang3

  • 1Department of Physical Education, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China.

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This study introduces a personalized recommendation system to help users find suitable outdoor sports amidst information overload. It combines multiple algorithms to offer effective health and leisure activity suggestions.

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Area of Science:

  • Sports Science
  • Computer Science
  • Information Science

Background:

  • Rapid economic development in China increases demand for effective stress relief and health improvement methods.
  • Outdoor sports are popular but network information is overwhelming, leading to information overload.
  • Personalized recommendation systems can address information overload by tailoring suggestions to user preferences.

Purpose of the Study:

  • To develop an effective personalized recommendation system for outdoor sports in China.
  • To address the challenge of information overload in online outdoor sports resources.
  • To improve user experience in discovering relevant outdoor activities.

Main Methods:

  • Summarized the current state of outdoor sports in China.
  • Studied recommendation system technologies including user modeling, target modeling, and algorithms.
  • Proposed a hybrid recommendation algorithm combining user-based collaborative filtering, item-based collaborative filtering, and content-based filtering.
  • Developed two hybrid models (Model 1 and Model 2) and combined them for improved performance.
  • Utilized content-based recommendation to solve the cold start problem.

Main Results:

  • A hybrid recommendation model was developed by combining user-based collaborative filtering, item-based collaborative filtering, and content-based approaches.
  • Feature expansion and weighted combination techniques were employed to enhance the recommendation effectiveness.
  • The final hybrid model demonstrated a superior recommendation effect compared to individual algorithms.
  • Content-based recommendation effectively mitigated the cold start issue.

Conclusions:

  • The proposed hybrid recommendation system effectively alleviates information overload for outdoor sports.
  • The developed system provides a valuable tool for individuals seeking personalized outdoor activity recommendations.
  • This approach offers a robust solution for recommending outdoor sports, enhancing user engagement and health benefits.