融合用户信任和用户兴趣漂移的协同过滤算法
|
摘要
针对现有的协同过滤算法推荐质量不高,提出了融合用户信任和用户兴趣的协同过滤算法CF-BI.首先根据用户历史评分矩阵,充分考虑用户偏好相似性、用户影响力和打分专业性等影响因子,提出综合用户偏好相似度和用户信誉度的信任模型;然后采用融入艾宾浩斯遗忘函数的Pearson相关系数计算用户间的兴趣相似度,通过加权融合获取用户信任与用户兴趣间的关联关系,以获取更加准确的最近邻居,并对目标用户采用Top-N算法进行推荐.在真实数据集MovieLens上的仿真实验结果表明,该算法的平均绝对误差比传统的协同过滤算法提升了16.98%,有效提高了推荐质量.
To solve this problem,the collaborative filtering algorithm based on user trust and user interest CF-BI was proposed.Firstly,according to the user's history score matrix,the trust model of the comprehensive user preference similarity and user credibility was proposed,which took full account of user preference similarity,user influence and scoring professional and other influencing factors;and then the similarity of users' interests was calculated by Pearson correlation coefficient of Ebbinghaus forgetting function,and the similarity degree and interest similarity degree between users were adjusted by the weighting coefficient,which made the selection of the nearest neighbor more accurate,and recommends the Top-N algorithm to the target users.Experimental results on the MovieLens dataset show that the average absolute error of the algorithm is 16.98% than that of the traditional collaborative filtering algorithm,which improves the quality of recommendation effectively.
To solve this problem,the collaborative filtering algorithm based on user trust and user interest CF-BI was proposed.Firstly,according to the user's history score matrix,the trust model of the comprehensive user preference similarity and user credibility was proposed,which took full account of user preference similarity,user influence and scoring professional and other influencing factors;and then the similarity of users' interests was calculated by Pearson correlation coefficient of Ebbinghaus forgetting function,and the similarity degree and interest similarity degree between users were adjusted by the weighting coefficient,which made the selection of the nearest neighbor more accurate,and recommends the Top-N algorithm to the target users.Experimental results on the MovieLens dataset show that the average absolute error of the algorithm is 16.98% than that of the traditional collaborative filtering algorithm,which improves the quality of recommendation effectively.
引文
[1]王立才,孟祥武,张玉洁.上下文感知推荐系统[J].软件学报,2012,23(1):1-20.
[2]郭宁宁,王宝亮,侯永宏,常鹏.融合社交网络特征的协同过滤推荐算法[J].计算机科学与探索,2018,12(02):208-217.
[3]OZSOY M G,POLAT F.Trust based recommendation systems[C]//Proceedings of the 17th International Conference on World Wide Web,New York:ACM,2013:1267-1274.
[4]邹本友,李翠平,谭力文,等.基于用户信任和张量分解的社会网络推荐[J].软件学报,2014,25(12):2852-2864.
[5]杨兴耀,于炯,吐尔根.依布拉音,等.基于信任模型填充的协同过滤推荐模型[J].计算机工程,2015,41(5):6-13.
[6]郝立燕,王靖.基于填充和相似性信任因子的协同过滤推荐算法[J].计算机应用,2013,33(3):834-837.
[7]陆坤,谢玲,李明楚.一种融合隐式信任的协同过滤推荐算法[J].小型微型计算机系统,2016,37(2):241-245.
[8]郑洁,钱育蓉,杨兴耀,等.基于信任和项目偏好的协调过滤算法[J].计算机应用,2016,36(10):2784-2788.
[9]LIY M,MUC T,LAIC Y.A social recommender mechanism for e-commerce combining similarity,trust and relationship[J].Decision Support Systems,2013,55(3):740-752.
[10]李克潮,梁正友.适应用户兴趣变化的指数遗忘协同过滤算法[J].计算机工程与应用,2011,47(13):154-156.
[11]印桂生,崔晓晖,马志强.遗忘曲线的协同过滤推荐模型[J].哈尔滨工程大学学报,2012,33(1):85-90.
[12]胡伟健,滕飞,李灵芳,等.适应用户兴趣变化的改进型协同过滤算法[J].计算机应用,2016,36(8):2087-2097.
[13]文俊浩,舒珊.一种改进相似性度量的协同过滤推荐算法[J].计算机科学,2014,41(5):68-71.
[14]于洪,李转运.基于遗忘曲线的协同过滤推荐算法[J].南京大学学报(自然科学),2010,46(5):520-527.
[15]江志恒,刘乃岑.论遗忘函数-关于记忆心理学的数学讨论[J].心理科学进展,1988(3):56-60.
[2]郭宁宁,王宝亮,侯永宏,常鹏.融合社交网络特征的协同过滤推荐算法[J].计算机科学与探索,2018,12(02):208-217.
[3]OZSOY M G,POLAT F.Trust based recommendation systems[C]//Proceedings of the 17th International Conference on World Wide Web,New York:ACM,2013:1267-1274.
[4]邹本友,李翠平,谭力文,等.基于用户信任和张量分解的社会网络推荐[J].软件学报,2014,25(12):2852-2864.
[5]杨兴耀,于炯,吐尔根.依布拉音,等.基于信任模型填充的协同过滤推荐模型[J].计算机工程,2015,41(5):6-13.
[6]郝立燕,王靖.基于填充和相似性信任因子的协同过滤推荐算法[J].计算机应用,2013,33(3):834-837.
[7]陆坤,谢玲,李明楚.一种融合隐式信任的协同过滤推荐算法[J].小型微型计算机系统,2016,37(2):241-245.
[8]郑洁,钱育蓉,杨兴耀,等.基于信任和项目偏好的协调过滤算法[J].计算机应用,2016,36(10):2784-2788.
[9]LIY M,MUC T,LAIC Y.A social recommender mechanism for e-commerce combining similarity,trust and relationship[J].Decision Support Systems,2013,55(3):740-752.
[10]李克潮,梁正友.适应用户兴趣变化的指数遗忘协同过滤算法[J].计算机工程与应用,2011,47(13):154-156.
[11]印桂生,崔晓晖,马志强.遗忘曲线的协同过滤推荐模型[J].哈尔滨工程大学学报,2012,33(1):85-90.
[12]胡伟健,滕飞,李灵芳,等.适应用户兴趣变化的改进型协同过滤算法[J].计算机应用,2016,36(8):2087-2097.
[13]文俊浩,舒珊.一种改进相似性度量的协同过滤推荐算法[J].计算机科学,2014,41(5):68-71.
[14]于洪,李转运.基于遗忘曲线的协同过滤推荐算法[J].南京大学学报(自然科学),2010,46(5):520-527.
[15]江志恒,刘乃岑.论遗忘函数-关于记忆心理学的数学讨论[J].心理科学进展,1988(3):56-60.