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Improved Recommendation Systems based on Transition Probability Vectors
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전이 확률 기반 벡터를 이용한 추천 시스템 성능 향상

논문 기본 정보

Type
Academic journal
Author
Wujin Cheon (고려대학교) Pilsung Kang (고려대학교)
Journal
Korean Institute Of Industrial Engineers Journal of the Korean Institute of Industrial Engineers Vol.46 No.4 KCI Excellent Accredited Journal
Published
2020.8
Pages
393 - 403 (11page)
DOI
10.7232/JKIIE.2020.46.4.393

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Improved Recommendation Systems based on Transition Probability Vectors
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Research history (2)

2020

Improved Recommendation Systems based on Transition Probability Vectors
Wujin Cheon ,  Pilsung Kang Journal of the Korean Institute of Industrial Engineers 2020.08 Academic journal
Improved recommendation systems based on transition probability vectors
천우진 산업경영공학과 2020.01 Thesis
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Abstract· Keywords

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Numerous companies are now able to store and manage huge amounts of information about their customers. Accordingly, studies on recommender systems are actively being conducted to use the information more efficiently. Among them, studies that wish to have high predictability using additional information other than purchase information are presented in this paper with a simple method to reduce costs and increase accuracy. The corresponding module is a vector based on the probability that an item is transferred to another item. Experiments conducted on public datasets show that the performances of the proposed architecture have improved by an average of 9.7% compared to the benchmark models. It was also intended to provide direction for cold-start problem resolution at no additional cost.
#Recommendation System #Transition Probability #Sequential Recommendation #Item Embedding

Contents

1. 서론
2. 관련 연구
3. 방법론
4. 실험
5. 결론
참고문헌

References (24)

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Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013), Translating Embeddings for Modeling Multi-relational Data, Advances in Neural Information Processing Systems, 2787-2795. google schola Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., and Zha, H. (2018), Sequential Recommendation with User Memory Networks, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 108-116. google schola Cho, E., Myers, S. A., and Leskovec, J. (2011), Friendship and Mobility : User Movement in Location-Based Social Networks, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1082-1090. google schola Cui, Q., Wu, S., Huang, Y., and Wang, L. (2019), A Hierarchical Contextual Attention-based Network for Sequential Recommendation, Neurocomputing, 358, 141-149. google schola Donkers, T., Loepp, B., and Ziegler, J. (2017), Sequential User-based Recurrent Neural Network Recommendations, Proceedings of the Eleventh ACM Conference on Recommender Systems, 152-160. google schola

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