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Collaborative filtering recommendation algorithm based on user preference derived from item domain features

Author

Listed:
  • Zhang, Jing
  • Peng, Qinke
  • Sun, Shiquan
  • Liu, Che

Abstract

Personalized recommendation is an effective method for fighting “information overload”. However, its performance is often limited by several factors, such as sparsity and cold-start. Some researchers utilize user-created tags of social tagging system to depict user preferences for personalized recommendation, but it is difficult to identify users with similar interests due to the differences between users’ descriptive habits and the diversity of language expression. In order to find a better way to depict user preferences to make it more suitable for personalized recommendation, we introduce a framework that utilizes item domain features to construct user preference models and combines these models with collaborative filtering (CF). The framework not only integrates domain characteristics into a personalized recommendation, but also aids to detecting the implicit relationships among users, which are missed by the conventional CF method. The experimental results show our method achieves the better result, and prove the user preference model is more effective for recommendation.

Suggested Citation

  • Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    • Handle: RePEc:eee:phsmap:v:396:y:2014:i:c:p:66-76
    DOI: 10.1016/j.physa.2013.11.013
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    References listed on IDEAS

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    Cited by:

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    6. Sang-Min Choi & Dongwoo Lee & Kiyoung Jang & Chihyun Park & Suwon Lee, 2023. "Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    7. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    8. Yong Eui Kim & Sang-Min Choi & Dongwoo Lee & Yeong Geon Seo & Suwon Lee, 2023. "A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    9. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.

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