지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 31 - 42 (12page)
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초록· 키워드
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During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. THE PROPOSED CASCADE-HYBRID RECOMMENDATION ALGORITHM
4. EVALUATION
5. CONCLUSION
REFERENCE
참고문헌 (16)
참고문헌 신청
J.L. Herlocker, J.A. Konstan, A.I. Borchers, and J. Riedl, “An Algorithmic Framework for Performaing Collaborative Filtering,” Proceedings of the 22nd Annual International Association for Computing Machinery Special interest Group on Information Retrieval Conference on Research and Development in Information Retrieval, pp. 230-237, 1999.
V.M. Robin and M.V. Someren, Using Content- based Filtering for Recommendation, Proceedings of the Machine Learning in the New Information Age: MLnet/ ECML2000 Workshop, pp. 47-56, 2000.
A. Nguyen, N. Denos, and C. Berrut, Improving New User Recommendations with Rulebased Induction on Cold User Data, Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 121-128, 2007.
X.N. Lam, T. Vu, T.D. Le, and A.D. Duong, Addressing Cold Start Problem in Recommendation Systems, Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication. ACM, pp. 208-211, 2008.
M. Nilashi, O.B. Ibrahim, and N. Ithnin, Hybrid Recommendation Approaches for Multi-criteria Collaborative Filtering, Expert Systems with Applications, Vol. 41, Issue 8, pp. 3879-3900, 2014.
V.M. Robin and M.V. Someren, Using Content- based Filtering for Recommendation, Proceedings of the Machine Learning in the New Information Age: MLnet/ ECML2000 Workshop, pp. 47-56, 2000.
A. Nguyen, N. Denos, and C. Berrut, Improving New User Recommendations with Rulebased Induction on Cold User Data, Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 121-128, 2007.
X.N. Lam, T. Vu, T.D. Le, and A.D. Duong, Addressing Cold Start Problem in Recommendation Systems, Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication. ACM, pp. 208-211, 2008.
M. Nilashi, O.B. Ibrahim, and N. Ithnin, Hybrid Recommendation Approaches for Multi-criteria Collaborative Filtering, Expert Systems with Applications, Vol. 41, Issue 8, pp. 3879-3900, 2014.
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UCI(KEPA) : I410-ECN-0101-2020-004-000361800