Browse > Article
http://dx.doi.org/10.21219/jitam.2022.29.3.043

Deep Learning-based Product Recommendation Model for Influencer Marketing  

Song, Hee Seok (Hannam University, Global IT Business)
Kim, Jae Kyung (Hannam University, Global IT Business)
Publication Information
Journal of Information Technology Applications and Management / v.29, no.3, 2022 , pp. 43-55 More about this Journal
Abstract
In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.
Keywords
Influencer Marketing; Product Recommendation; Deep Learning; Collaborative Filtering; One-class Problem;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Song, H. S., "Deep neural network-based beauty product recommender", Journal of Information Technology Application & Management, Vol. 26, No. 6, 2019, pp. 89-101.
2 Wang, Y., Chuang, Y., Hsu, M., and Keh, H., "A personalized recommender system for the cosmetic business", Expert Systems with Applications, Vol. 26, 2004, pp. 427-434.   DOI
3 Lee, E., Song, J., Kim, I., and Yoo, J., "Big-data analysis based mobile services using individual skin-type and genes for cosmetic recommendation", Proceedings of Korea Contents Society, 2018, pp. 495-496.
4 Okura, S., Tagami, Y., Ono, S., and Tajima, A., "Embedding-based news recommendation for millions of users", In SIGKDD, 2017, pp. 1933-1942.
5 Song, G. and Song. H., "Algorithm for generating negative cases for collaborative filtering recommender", Expert Systems, https://doi.org/10.1111/exsy.12986, 2022.   DOI
6 Kim, S., Kim, E., and Ki, Y., "Cosmetic recommendation system using fuzzy inference and buildnig sentiment dictionary", Journal of Korean Institute of Intelligent Systems, Vol. 27, No. 3, 2017, pp. 253-260.   DOI
7 He, X. and Chua, T., "Neural factorization machines for sparse predictive analytics", Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017.
8 Herlocker, J., Konstan, J. A., and Riedl, J., "An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms", Information Retrieval, Vol. 5, No. 4, 2002.
9 Iwabuchi, R., Nakajima, Y., Honma, H., Aoshima, H., Kobayashi, A., and Akiba, T., "Proposal of recommender system based on user evaluation and cosmetic ingredients", 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), 2017, pp. 1-6.
10 Jung, A. Y., "Influencer marketing case analysis and marketing research proposal", Service Marketing Journal, Vol. 12, No. 1, 2019, pp. 33-39.   DOI
11 Choi, D. J., Yoo, S. H., Seo, I. D., Jeong, J. Y., Song, H. S., Park, J. Y., Song, J. O, Bok, K. S. and Yoo, J. S., "Design of a trend analysis and recommendation system using beauty big data", Proceedings of Symposium of the Korean Institute of communications and Information Sciences, 2018, pp. 1520-1521.
12 Koren, Y., Bell, R., and Volinsky, C., "Matrix factorization techniques for recommender systems", Computer, Vol. 42, No. 8, 2009, pp. 30-37.   DOI
13 Bokde, D., Girase, S., and Mukhopadhyay, D., "Matrix factorization model in collaborative filtering algorithms: A survey", Prodedia Computer Science, Vol. 49, 2015, pp. 136-146.   DOI
14 Chee, S. H. S., Han, J., and Wang, K., "RecTree: An Efficient Collaborative Filtering Method", Data Warehousing and Knowledge Discovery, 2001, pp. 141-151.
15 Cheng, H., Koc, L., Harmsen, J., and Shaked, T., "Wide & deep learning for recommender systems", Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016, pp. 7-10.
16 Yim, Y., Bae, H., Jeong, Y., Kim, M., Nasridinov, A., Yoo, K. H., and Hong, J., "A user driven cosmetic item recommendation system by character recognition", Proceeding of Korea Information Processing Society, 2016, pp. 722-725.
17 Cho, Y. S., Gu, M. S., and Tyu, K. H., "Development of personalized recommendation system using RFM method and k-means clustering", Journal of The Korea Society of Computer and Information, Vol. 17, No. 6, 2012.
18 Gholamian, M., Fathian, J., M., and Mehrbod, A., "Improving electronic customers' profile in recommender systems using data mining techniques", Management Science Letters, Vol. 1, No. 4, 2011, pp. 449-456.   DOI
19 Covington, P., Adams, J., and Sargin, E., "Deep neural networks for youtube recommendations", Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 191-198.
20 Ha, E., Moon, J., and Hwang, E., "User's SNS data-based scoring scheme for personalized cosmetics recommendation", Preceeding of Korea Information Processing Society, Vol. 23, No. 2, 2016.
21 He, X., Liao, L., Zhang., H., Nie, L., Hu, X., and Chua, T., "Neural collaborative filtering", Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 173-182.
22 Patty, J. C., Kirana, E. T., and Giri, M. S. D. K, "Recommendations system for purchase of cosmetics using content-based filtering", International Journal of Computer Engineering and Information Technology, Vol. 10, No. 1, 2018, pp. 1-5.
23 Lee, Y., Yang, H., Choi, J., and Hur, J., "A study on comparison between association rule and collaborative filtering using the factor and k-means cluster analysis in the recommendation of cosmetics", Journal of the Korean Data Analysis Society, Vol. 14, No. 2, 2012, pp. 689-705.
24 Matsunami, Y., Ueda, M., and Nakajima, S., "How to find similar users in order to develop a cosmetics recommender system", Transactions on Engineering Technologies, 2018, pp. 337-350.