Browse > Article
http://dx.doi.org/10.21219/jitam.2018.25.4.145

Nutrient Profiling-based Pet Food Recommendation Algorithm  

Song, Hee Seok (Department of Global IT Business in Hannam University)
Publication Information
Journal of Information Technology Applications and Management / v.25, no.4, 2018 , pp. 145-156 More about this Journal
Abstract
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.
Keywords
Recommender; Pet Food; Content-Based Recommendation Algorithm; Nutrient Profiling;
Citations & Related Records
연도 인용수 순위
  • Reference
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.