CAJ | 학술논문

协同过滤推荐算法通过研究用户的喜好，实现从海量数据资源中为用户推荐其感兴趣的内容，在电子商务中得到了广泛的应用。然而，当此类算法应用到社交网络时，传统的评价指标与相似度计算的重点发生了变化，从而出现推荐算法效率偏低，推荐准确度下降问题，导致社交网络中用户交友推荐满意度偏低。针对这一问题，引入用户相似度概念，定义社交网络中属性相似度，相似度构成与计算方法，提出一种改进的协同过滤推荐算法，并给出推荐质量与用户满意度评价方法。实验结果表明：改进算法能有效改善社交网络中的推荐准确性并提高推荐效率，全面提高用户满意度。
협동과려추천산법통과연구용호적희호，실현종해량수거자원중위용호추천기감흥취적내용，재전자상무중득도료엄범적응용。연이，당차류산법응용도사교망락시，전통적평개지표여상사도계산적중점발생료변화，종이출현추천산법효솔편저，추천준학도하강문제，도치사교망락중용호교우추천만의도편저。침대저일문제，인입용호상사도개념，정의사교망락중속성상사도，상사도구성여계산방법，제출일충개진적협동과려추천산법，병급출추천질량여용호만의도평개방법。실험결과표명：개진산법능유효개선사교망락중적추천준학성병제고추천효솔，전면제고용호만의도。
Collaborative filtering recommendation algorithms widely used in e-commerce, recommend interesting content for users from massive data resources by studying their preferences and interests. The focus of similarity and evaluation have been changed when applied to social networks, however, they cause low efficiency and accuracy of the recommen-dation algorithms. User similarity was introduced for redefining the attribute similarity and similarity composition as well as the method of similarity calculating, then a new collaborative filtering recommendation algorithm based on user attributes was designed and some methods for user satisfaction and quality of recommendations were presented. The ex-perimental result shows that the new algorithm can effectively improve the accuracy, quality and user satisfaction of recommendation system in social networks.