• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Digital Library


Search: "[ keyword: 추천 시스템 ]" (11)
  1. 1. Development of Product Recommendation System Using MultiSAGE Model and ESG Indicators
    Hyeon-woo Kim, Yong-jun Kim, Gil-sang Yoo, Vol. 25, No. 1, pp. 69-78, Feb. 2024
    10.7472/jksii.2024.25.1.69
    Keywords: MultiSAGE, recommend system, ESG, GraphSAGE
  2. 2. A Comparison Study of RNN, CNN, and GAN Models in Sequential Recommendation
    Ji Hyung Yoon, Jaewon Chung, Beakcheol Jang, Vol. 23, No. 4, pp. 21-33, Aug. 2022
    10.7472/jksii.2022.23.4.21
    Keywords: Recommender System, RNN, CNN, GAN, Deep Learning, sequence modeling
  3. 3. A Study on Factors Affecting University Students' Satisfaction with YouTube AI Recommendation System
    LiuCun Zhu, Chao Wang, HaSung Hwang, Vol. 23, No. 3, pp. 77-85, Jun. 2022
    10.7472/jksii.2022.23.3.77
    Keywords: YouTube AI recommendation system, Satisfaction, TAM, Usefulness, preference suitability, privacy concerns
  4. 4. A Study of Recommendation Systems for Supporting Command and Control (C2) Workflow
  5. 5. The Technique of Reference-based Journal Recommendation Using Information of Digital Journal Subscriptions and Usage Logs
  6. 6. Clustering Algorithm using the DFP-Tree based on the MapReduce
  7. 7. Method to Improve Data Sparsity Problem of Collaborative Filtering Using Latent Attribute Preference
    Hyeong-Joon Kwon, Kwang-Seok Hong, Vol. 14, No. 5, pp. 59-68, Oct. 2013
    10.7472/jksii.2013.14.5.59
    Keywords: Collaborative Filtering, Attribute Preference, Recommender System
  8. 8. Attack Detection in Recommender Systems Using a Rating Stream Trend Analysis
    Yong-Uk Kim, Jun-Tae Kim, Vol. 12, No. 2, pp. 85-102, Apr. 2011
    Keywords: Recommendation System, Recommendation Attack, data stream
  9. 9. Personalization of LBS using Recommender Systems Based on Collaborative Filtering
    Hyeong-Joon Kwon, Kwang-Seok Hong, Vol. 11, No. 6, pp. 1-12, Dec. 2010
  10. 10. A Robust Collaborative Filtering against Manipulated Ratings