Journal of Information Technology Applications and Management

  • 제25권4호
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  • Pages.145-156
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  • 2018
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  • 1598-6284(pISSN)
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  • 2508-1209(eISSN)
한국데이터전략학회 (Korea Data Strategy Society)
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영양성분 프로파일링 기반 사료추천 알고리듬

Nutrient Profiling-based Pet Food Recommendation Algorithm

  • Song, Hee Seok (Department of Global IT Business in Hannam University)
  • 투고 : 2018.11.28
  • 심사 : 2018.12.26
  • 발행 : 2018.12.31
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초록

This study proposes a content-based recommendation algorithm (NRA) for pet food. The proposed algorithm tries to recommend appropriate or inappropriate feed by using collective intelligence based on user experience and prior knowledge of experts. Based on the physical and health status of the dogs, this study suggests what kind of nutrients are necessary for the dogs and the most recommended pet food containing these nutrients. Performance evaluation was performed in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC and F1 value of the proposed NRA was 15% and 42% higher than that of the baseline model, respectively. In addition, the performance of NRA is shown higher for recommendation of normal dogs than disease dogs.

키워드

DOTSBL_2018_v25n4_145_f0001.png 이미지

The Process of Performance Evaluation

DOTSBL_2018_v25n4_145_f0002.png 이미지

Performance Comparison by Model

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Performance Applied to Training set by Dog Type

DOTSBL_2018_v25n4_145_f0004.png 이미지

Performance Applied to Test Set by Dog Type

참고문헌

  1. Almeida, D., Personalized Food Recommendations, Dissertation of MS, Tecnico Risboa, 2015.
  2. Bengio, Y., Simard, P., and Frasconi, P., "Learning long-term dependencies with gradient descent is difficult", IEEE Transactions on Neural Networks, Vol. 5, No. 2, 1994, pp. 157-166.
  3. Forbes, P. and Zhu, M., "Content-boosted Matrix Factorization for Recommender Systems: Experiments with Recipe Recommendation", RecSys 2011 Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 261-264.
  4. Freyne, J. and Berkovsky, S., "Recommending food: Reasoning on recipes and ingredients", In Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization, Vol. 6075, 2010, pp. 381-386.
  5. Koren, Y. and Sill, J., "Collaborative Filtering on Ordinal User Feedback", ACM Conference on Recommendation Systems (RecSys'11), 2011.
  6. Lin, C. J., Kuo, T. T., and Lin, S. D., "A Content-Based Matrix Factorization Model for Recipe Recommendation", Advances in Knowledge Discovery and Data Mining, Vol. 8444, 2014.
  7. Pazzani, M. J. and Billsus, D., "Content-Based Recommendation Systems", The Adaptive Web, Vol. 4321, 2007, pp. 325-341.
  8. Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L., "Factorizing personalized markov chains for next-basket recommendation", In WWW Conference, 2010, pp. 811-820.
  9. Sato, M., Izumo, H., and Sonoda, T., "Discount Sensitive Recommender System for Retail Business", Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems, 2015, pp. 33-40.
  10. Ueda, M., Takahata, M., and Nakajima, S., "User's food preference extraction for personalized cooking recipe recommendation", In CEUR Workshop Proceedings, Vol. 781, 2011, pp. 98-105.
  11. Wang, J., Sarwar, B., and Sundaresan, N., "Utilizing related products for post-purchase recommendation in e-commerce", Proceedings of the fifth ACM Conference on Recommender Systems, 2011, pp. 329-332.