面向电影推荐的时间加权协同过滤算法的研究

doi:10.11896/j.issn.1002-137X.2017.04.060

摘要/Abstract

摘要： 针对协同过滤算法的信息过期问题,提出一种改进的时间加权协同过滤算法(NTWCF)。考虑信息随时间推移导致信息影响力变化的因素,在信息半衰期的启发下,引入信息保持期的概念,通过在最近邻查找阶段和预测评分阶段采用一种新颖的时间加权函数为项目上的评分赋予不同的时间权重。电影数据集上的实验结果表明,它在一定程度上大幅度提高了预测推荐的准确性。接着,针对算法的实时性问题,利用时间加权的项目聚类优化NTWCF算法,提出综合时间权重和项目聚类的协同过滤算法(TWICCF),对评分信息时间加权后再对项目K-means聚类,在为目标项目查找最近邻时只在若干聚类构成的项目集中进行。相比NTWCF算法, TWICCF算法在推荐准确度和实时性上均取得了显著的提升。

Abstract: In order to deal with the outdated information problem of collaborative filtering algorithm,a new time weighted collaborative filtering algorithm (NTWCF) was proposed.Considering the influence of this change,it introduced the concept of information retention period which inspired by the information half-value period.At the stages of nearest neighbor searching and predictive scoring,this paper used a novel time weighted function to put time weight to the user’sscore.The experimental results of movie data sets show that it can greatly improve the accuracy of predicted ratings.Then,in order to improve the real-time speed of algorithm,this paper used time weighted item clustering algorithm to optimize NTWCF,and put forward a collaborative filtering algorithm based on time weight and item clustering (TWICCF).It used K-means to cluster the items based on time weighted scores,and then searched the nearest neighbors of the target item in the set of some clusters.TWICCF algorithm achieves significant improvements in both recommendation accuracy and real-time than NTWCF algorithm.

Key words: Collaborative filtering,Movie recommendation,Information half-value period,Information retention period,Time weighted,Item clustering

兰艳,曹芳芳. 面向电影推荐的时间加权协同过滤算法的研究[J]. 计算机科学, 2017, 44(4): 295-301. https://doi.org/10.11896/j.issn.1002-137X.2017.04.060

LAN Yan and CAO Fang-fang. Research of Time Weighted Collaborative Filtering Algorithm in Movie Recommendation[J]. Computer Science, 2017, 44(4): 295-301. https://doi.org/10.11896/j.issn.1002-137X.2017.04.060

参考文献

[1] GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,1992,35(12):61-70.
[2] RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]∥Proceedings of the 1994 ACM conference on Computer supported cooperative work.New York:ACM,1994:175-186.
[3] GREG L,BRENT S,JEREMY Y.Amazon.com recommendations:Item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[4] SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques[J].Advances in Artificial Intelligence,2009,2009:4.
[5] WEI S Y,YE N,ZHANG S,et al.Collaborative filtering recommendation algorithm based on item clustering and global similarity[C]∥2012 Fifth International Conference on Business Intelligence and Financial Engineering (BIFE).IEEE,2012:69-72.
[6] OU L Q,CHEN L,MA Y.Study on New Item Recommenda-tion Method in Collaborative Filtering Algorithm[J].Microcomputer Information,2005,1(11X):186-187.(in Chinese) 欧立奇,陈莉,马煜.协同过滤算法中新项目推荐方法的研究[J].微计算机信息,2005,1(11X):186-187.
[7] DING Y,LI X.Time weight collaborative filtering[C]∥ Proceedings of the 14th ACM International Conference on Information and Knowledge Management.ACM,2005:485-492.
[8] POLAT H,DU W.SVD-based collaborative filtering with privacy[C]∥Proceedings of the 2005 ACM Symposium on Applied Computing.ACM,2005:791-795.
[9] MELVILLE P,MOONEY R J,NAGARAJAN R.Content-boosted collaborative filtering for improved recommendations[C]∥AAAI/IAAI.Edmonton,Canda,2002:187-192.
[10] UNGAR L H,FOSTER D P.Clustering methods for collaborative filtering[C]∥AAAI Workshop on Recommendation Systems.2000.
[11] GONG S J.A collaborative filtering recommendation algorithm based on user clustering and item clustering[J].Journal of Software,2010,5(7):745-752.
[12] WANG X,YU Z,WANG C.Recommendation with Item Clustering Based Collaborative Filtering[C]∥2014 International Conference on Computer Science and Electronic Technology (ICCSET 2014).Atlantis Press,2015.
[13] WEI S Y,YE N,ZHU J,et al.Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity[J].Computer Science,2012,9(12):149-152.(in Chinese) 韦素云,业宁,朱健,等.基于项目聚类的全局最近邻的协同过滤算法[J].计算机科学,2012,9(12):149-152.
[14] LIU D H,PENG D W,ZHANG H.Collaborative Filtering Algorithm Based on Time Weight and User’s Feature[J].Journal of Wuhan University of Technology,2012,4(5):144-148.(in Chinese) 刘东辉,彭德巍,张晖.一种基于时间加权和用户特征的协同过滤算法[J].武汉理工大学学报,2012,4(5):144-148.
[15] DENG J,CHEN X Q.Study of Collaborative Filtering Recommendation Algorithm Based on Users’ Interest Change[J].Journal of Wuhan Polytechnic University,2013,2(4):48-51.(in Chinese) 邓娟,陈西曲.基于用户兴趣变化的协同过滤推荐算法[J].武汉工业学院学报,2013,2(4):48-51.
[16] XING C X,GAO F R,ZHAN S N,et al.Collaborative filtering recommendation algorithm incorporated with user interest change[J].Computer Research and Development,2007,44(2):296-301.
[17] ZENG D H,WANG T,YAN S F,et al.One Collaborative Filtering Recommendation Algorithm Based on Exponential Forgetting Function[J].Science Mosaic,2013(7):10-15.(in Chinese) 曾东红,汪涛,严水发,等.一种基于指数遗忘函数的协同过滤算法[J].科技广场,2013(7):10-15.
[18] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥ Procee-dings of the 10th International Conference on World Wide Web.ACM,2001:285-295.
[19] ZHENG C C,LI L.Research on method of similarity measurement in collaborative filter algorithm[J].Computer Engineering and Applications,2014,50(8):147-149.(in Chinese) 郑翠翠,李林.协同过滤算法中的相似性度量方法研究[J].计算机工程与应用,2014,0(8):147-149.
[20] GRABTREE I,SOLTYSIAK S.Identifying and Tracking Changing Interests[J].International Journal of Digital Libraries,1998,2(1):38-53
[21] KOYCHEV I,SCHWAB I.Adaptation to Drifting User’s Inte-rests [C]∥Proceedings of ECML 2000 Worksbop:Machine Learning in New Information Age.Barcelona,Spain,2000:39-46.
[22] KOYCHEV I.Gradual Forgetting for Adaptation to ConceptDrift[C]∥Proceedings of ECAI 2000.2000.
[23] XING C X,GAO F R,ZHAN X S,et al.A Collaborative Filtering Recommendation Algorithm Incorporated with User Inte-rest Change[J].Journal of Computer Research and Development,2007,4(2):296-301.(in Chinese) 邢春晓,高凤荣,战思南,等.适应用户兴趣变化的协同过滤推荐算法[J].计算机研究与发展,2007,4(2):296-301.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed