计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
12期
4217-4222
,共6页
推荐系统%协同过滤%平均相似度%平均绝对偏差%个性化推荐
推薦繫統%協同過濾%平均相似度%平均絕對偏差%箇性化推薦
추천계통%협동과려%평균상사도%평균절대편차%개성화추천
recommender system%collaborative filtering%average similarity%MAE%personalized recommendation
针对CF推荐技术依赖的评分矩阵在现实中存在的稀疏性问题,提出用户‐项目平均相似度协同过滤推荐算法(ASUCF)。对评分矩阵进行充分挖掘、多次利用,引入平均相似度来惩罚用户或项目的评分或被评分的波动;综合考虑用户和项目两方面,提高预测评分的可靠性。实验结果表明,该方法可以有效提高预测的准确性及推荐质量。
針對CF推薦技術依賴的評分矩陣在現實中存在的稀疏性問題,提齣用戶‐項目平均相似度協同過濾推薦算法(ASUCF)。對評分矩陣進行充分挖掘、多次利用,引入平均相似度來懲罰用戶或項目的評分或被評分的波動;綜閤攷慮用戶和項目兩方麵,提高預測評分的可靠性。實驗結果錶明,該方法可以有效提高預測的準確性及推薦質量。
침대CF추천기술의뢰적평분구진재현실중존재적희소성문제,제출용호‐항목평균상사도협동과려추천산법(ASUCF)。대평분구진진행충분알굴、다차이용,인입평균상사도래징벌용호혹항목적평분혹피평분적파동;종합고필용호화항목량방면,제고예측평분적가고성。실험결과표명,해방법가이유효제고예측적준학성급추천질량。
In the user‐item rating matrix which is relied on by collaborative filtering ,there exists the problem of data sparsity . For this problem ,a kind of improved model called ASUCF was proposed .The matrix was sufficiently exploited and repeatedly used .The average similarity was used to punish the fluctuations of user’s ratings or item’s score ,and the reliability of prediction score from users as well as items was improved .Finally ,the experiments prove that the algorithm can effectively improve the ac‐curacy of prediction and recommendation quality .