基于用户习惯偏好相似度的Slope One推荐算法
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摘要
数据稀疏是协同过滤预测精度的一个重要影响因素。Slope One算法使用简单的线性回归模型解决该问题,但它只使用评分数据做计算,未考虑相似性。提出一种基于用户习惯偏好相似度的Slope One算法(UPSSlope One)。UPS Slope One首先基于用户习惯偏好聚类,得到三组不同偏好的用户,然后分别计算各组评分偏差,计算时将用户习惯偏好相似度融入其中,最后使用线性回归模型预测评分。在MovieLens数据集上的实验表明,该算法可得到更高的推荐质量、预测准确性和稳定性。
Data sparsity is a main factor affecting the prediction accuracy of collaborative filtering. Slope One algorithm uses simple linear regression model to solve this problem. It uses rating data to do calculation without considering the similarity. In this paper,we proposed an improved Slope One algorithm based on user preference similarity. First,we obtain three groups of users with different preferences based on the user preference clustering,and then calculate the deviation of each group,we add the user preference similarity to deviation calculation,finally use linear regression model to predict the rating. Experiments on the MovieLens data set show that the proposed algorithm can achieve better recommendation quality and prediction accuracy. Meanwhile,the stability of the algorithm is also relatively satisfying.
Data sparsity is a main factor affecting the prediction accuracy of collaborative filtering. Slope One algorithm uses simple linear regression model to solve this problem. It uses rating data to do calculation without considering the similarity. In this paper,we proposed an improved Slope One algorithm based on user preference similarity. First,we obtain three groups of users with different preferences based on the user preference clustering,and then calculate the deviation of each group,we add the user preference similarity to deviation calculation,finally use linear regression model to predict the rating. Experiments on the MovieLens data set show that the proposed algorithm can achieve better recommendation quality and prediction accuracy. Meanwhile,the stability of the algorithm is also relatively satisfying.
引文
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[15]杜茂康,刘苗,李韶华,等.基于邻近项目的Slope One协同过滤算法[J].重庆邮电大学学报:自然科学版,2014,26(3):421-426.
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[20]CHAI H,LIU J Y.An improved slope one recommendation algorithm[J].Security&Informatization,2015(6):77-81.
[21]LI J,SUN L,WANG J.A slope one collaborative filtering recommendation algorithm using uncertain neighbors optimizing[C].International Conference on Web-Age Information Management.Wu Han:Springer-Verlag Berlin,2011:160-166.
[22]WANG J,JUN J,VRIES D,et al.Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C].Proc.of,ACM SIGIR Conference on Information Retrieval.Washington:ACM,2006:501-508.
[2]JIA C X,LIU R R.Improve the algorithmic performance of collaborative filtering by using the interevent time distribution of humanbehaviors[J].Physica A Statistical Mechanics&Its Applications,2015(436):236-245.
[3]KOOHBORFARDHAGHIGHI,SOMAYEH,KIM,et al.Using structural information for distributed recommendation in a social;network[J].Applied Intelligence,2013,38(2):255-266.
[4]HERLOCKER J,KONSTAN J A,RIEDL J.An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms[J].Information Retrieval,2002,5(4):287-310.
[5]黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8):1369-1377.
[6]HUANG C G,YIN J,WANG J,et al.Uncertain neighbors’collaborative filtering recommendation algorithm[J].Chinese Journal of Computers,2010,33(8):1369-1377.
[7]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C].International Conference on World Wide Web,Hong Kong:ACM,2001:285-295.
[8]SHI Y,LARSON M,HANJALIC A.Collaborative filtering beyond the user-item matrix:a survey of the state of the art and future challenges[J].ACM Computing Surveys,2014,47(1):1-45.
[9]LIU Q,CHEN E,XIONG H,et al.Enhancing collaborative filtering by user interest expansion via personalized ranking[J].IEEE Trans Syst Man Cybern B Cybern,2012,42(1):218-233.
[10]ZHU T,REN Y,ZHOU W,et al.An effective privacy preserving algorithm for neighborhood-based collaborative filtering[J].Future Generation Computer Systems,2014,36(36):142-155.
[11]JAMALI M,ESTER M.Trustwalker:a random walk model for combining trust-based and item-based recommendation[C].ACM SIG-KDD International Conference on Knowledge Discovery and Data Mining.Paris:ACM,2009:397-406.
[12]AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.
[13]LIU H,HU Z,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Systems,2014,56(3):156-166.
[14]ZHANG J,LIN Y,LIN M,et al.An effective collaborative filtering algorithm based on user preference clustering[J].Applied Intelligence,2016,45(2):230-240.
[15]杜茂康,刘苗,李韶华,等.基于邻近项目的Slope One协同过滤算法[J].重庆邮电大学学报:自然科学版,2014,26(3):421-426.
[16]DU M K,LIU M,LI S H,et al.Slope one collaborative filtering algorithm based on neighboring items[J].Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2014,26(3):421-426.
[17]林德军,孟祥武.基于奇异值分解的Slope One算法[J].新型工业化,2012,2(11):12-17.
[18]LIN D J,MENG X W.Slope one algorithm based on single value decomposition[J].Journal of New Industrialization,2012,2(11):12-17.
[19]柴华,刘建毅.一种改进的slope one推荐算法研究[J].网络安全和信息化,2015(3):77-81.
[20]CHAI H,LIU J Y.An improved slope one recommendation algorithm[J].Security&Informatization,2015(6):77-81.
[21]LI J,SUN L,WANG J.A slope one collaborative filtering recommendation algorithm using uncertain neighbors optimizing[C].International Conference on Web-Age Information Management.Wu Han:Springer-Verlag Berlin,2011:160-166.
[22]WANG J,JUN J,VRIES D,et al.Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C].Proc.of,ACM SIGIR Conference on Information Retrieval.Washington:ACM,2006:501-508.