CAJ | 학술논문

协同过滤推荐算法是在电子商务推荐系统中应用最成功的推荐技术之一.提出了一种基于共同评分和相似性权重的协同过滤推荐算法.该算法选择用户的共同评分数据计算用户的相似性,选择项目被用户共同评分的数据计算项目的相似性,再分别计算基于用户以及项目算法的预测评分,然后通过相似性权重结合两者得到最终的预测结果,最后再根据预测结果产生推荐.实际数据的实验结果表明,提出的算法显著提高了预测准确度,从而提高了推荐质量.
협동과려추천산법시재전자상무추천계통중응용최성공적추천기술지일.제출료일충기우공동평분화상사성권중적협동과려추천산법.해산법선택용호적공동평분수거계산용호적상사성,선택항목피용호공동평분적수거계산항목적상사성,재분별계산기우용호이급항목산법적예측평분,연후통과상사성권중결합량자득도최종적예측결과,최후재근거예측결과산생추천.실제수거적실험결과표명,제출적산법현저제고료예측준학도,종이제고료추천질량.
Collaborative filtering recommendation algorithm is one of the most successful technologies in the e-commerce recommendation system.This paper presented a collaborative filtering algorithm based on co-ratings and similarity weight.First,the co-ratings were selected to compute the similarity between users or items.Most importantly,the algorithm acquiring the last prediction result was acquired by combining prior predicting rating with similarity weight,from which recommendation was produced.The experimental results in real data show this algorithm can consistently achieve better prediction accuracy,thereby brings better recommendation quality.