Computer Science ›› 2016, Vol. 43 ›› Issue (12): 223-228.doi: 10.11896/j.issn.1002-137X.2016.12.041

Previous Articles     Next Articles

Recommending Commodities Based on User-browsing Tracks

GUO Jun-xia, XU Wen-sheng and LU Gang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: With the rapid development of E-commerce,recommendation system has been widely used in the Websites.Currently the collaborative filtering recommendation algorithm is the most widely used,however,this kind of methods has sparse matrix and cold-start problems.In order to solve or at least improve these problems,methods based on users’ browsing records were proposed.These methods extract every user’s browsing path sequence called user browsing tracks from the users’ access log,and then recommend preference commodities for the user based on the analyzing result of the tracks.By now,the most methods that recommend commodities for users through analysis browsing path are based on sequence pattern matching or the view of the relationship between commodity and the next browsed commodity.We considered from the view of the relationship between browsing commodities and eventually bought commodities,establishing the user browsing tracks preference model based on this,mining users’ preference,and recommending products for new users.Experiments show that our method plays a certain role in solving the problem of cold-start for new users and enhancing the accuracy and recall rate of the recommendation system in E-commerce.

Key words: Personalized recommendation,Browsing tracks,Cold-start

[1] Pazzani M J,Billsus D.Content-Based Recommendation Systems[M]∥The Adaptive Web.Springer Berlin Heidelberg,2007:325-341
[2] Sarwar B,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Proceedings of International Conference on World Wide Web.2001:285-295
[3] Schein A I,Popescul A,Ungar L H,et al.Methods and metrics for cold-start recommendations[C]∥Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2002:253-260
[4] Lam X N,Vu T,Le T D,et al.Addressing cold-start problem in recommendation systems[C]∥Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication.ACM,2008:208-211
[5] Song Y.Personalized Search Engine Based on the User Behavior Analysis[J].New Century Library,2013
[6] Ye Y.Research on Interest Model of User Behavior[D].Shanghai:East China University,2012(in Chinese) 叶彧.基于用户行为的兴趣模型的研究[D].上海:东华大学,2012
[7] Sun X H.Research on sparsity and cold start of collaborative filtering system[D].Hangzhou:Zhejiang University,2005(in Chinese) 孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].杭州:浙江大学,2005
[8] Huang G Q,Zhao Y M.Approach to collaborative filtering re-commendation based on HMM[J].Journal of Computer Applications,2008,28(6):1601-1604(in Chinese) 黄光球,赵永梅.基于HMM模型的协同过滤推荐方法[J].计算机应用,2008,28(6):1601-1604
[9] Juan B.Collaborative filtering recommendation algorithm based on semantic similarity of item[C]∥IEEE Fifth International Conference on Advanced Computational Intelligence.2012:452-454
[10] Fan M,Zhou Q,Zheng T F.Content-Based Semantic Tag Ran-king for Recommendation[C]∥2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.IEEE Computer Society,2012:292-296
[11] Yao L,Sheng Q Z,Segev A,et al.Recommending Web Services via Combining Collaborative Filtering with Content-Based Features[C]∥2013 IEEE 20th International Conference on Web Services.IEEE,2013:42-49
[12] Popescul A,Ungar L H,Pennock D M,et al.Probabilistic Mo-dels for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments[C]∥ Proceedings of the 17th Conference on Univertointy in Artificial Intelligence.2013:437-444
[13] Chu W,Park S T.Personalized recommendation on dynamic content using predictive bilinear models[C]∥Proceedings of the 18th International Conference on World Wide Web.ACM,2009:691-700
[14] Wu T,He H,Gu X,et al.An intelligent network user behavior analysis system based on collaborative Markov model and distributed data processing[C]∥2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).IEEE,2013:221-228
[15] Gong M,Xu Z,Xu L,et al.Recommending Web Service Based on User Relationships and Preferences[C]∥IEEE International Conference on Web Services.2013:380-386
[16] Sobhanam H,Mariappan A K.Addressing cold start problem in recommender systems using association rules and clustering technique[C]∥International Conference on Computer Communication & Informatics.IEEE,2013:1-5
[17] Zhang D,Hsu C,Chen M,et al.Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems[J].IEEE Transactions on Emerging Topics in Computing,2014,2(2):239-250
[18] Park S T,Pennock D,Madani O,et al.Na07ve filterbots for robust cold-start recommendations[C]∥Proc of Acm Sigkdd Int’l Conf.2006:699-705
[19] Che G Y,Zhang L,Zhang L X.User Browsing Behavior Extraction and Analysis Based on Sequence Pattern[J].Computer Technology & Development,2012,22(9):9-12(in Chinese) 车高营,张磊,张禄旭.基于序列模式的用户浏览行为提取与分析[J].计算机技术与发展,2012,22(9):9-12
[20] Yap G E,Li X L,Yu P S.Effective next-items recommendation via personalized sequential pattern mining[C]∥Proceedings of the 17th International Conference on Database Systems for Advanced Applications-Volume Part II.Springer-Verlag,2012:48-64
[21] Li Y,Niu Z,Chen W,et al.Combining collaborative filtering and sequential pattern mining for recommendation in e-learning environment[C]∥International Conference on Advances in Web-based Learning.Springer-Verlag,2011:305-313
[22] Choi K,Yoo D,Kim G,et al.A hybrid online-product recom-mendation system:Combining implicit rating-based collaborative filtering and sequential pattern analysis[J].Electronic Commerce Research & Applications,2012,11(4):309-317
[23] Chen W,Niu Z,Zhao X,et al.A hybrid recommendation algorithm adapted in e-learning environments[J].World Wide Web-internet & Web Information Systems,2014,17(2):1-14
[24] Zhou Y N,Zheng H S.Ant Collaborative Filtering Based on Options of Browsing Path:Used for M-commerce Personalized Re-commendation System[C]∥Systems Engineering Society of China.2012(in Chinese) 周玉妮,郑会颂.基于浏览路径选择的蚁群推荐算法:用于移动商务个性化推荐系统[C]∥中国系统工程学会学术年会.2012
[25] Khan S,Baig A R,Shahzad W.A novel ant colony optimization based single path hierarchical classification algorithm for predicting gene ontology[J].Applied Soft Computing,2014,16(3):34-49
[26] Mohanthy R,Naik V,Mubeen A.Software Reliability Prediction by Using Ant Colony Optimization Technique[C]∥Fourth International Conference on Communication Systems & Network Technologies.IEEE Computer Society,2014:496-500
[27] Singh L K,Vinod G,Tripathi A K.Approach for parameter estimation in Markov model of software reliability for early prediction:A case study[J].Iet Software,2015,9(3):65-75
[28] Sampathkumar H,Chen X W,Luo B.Mining Adverse Drug Reactions from online healthcare forums using Hidden Markov Model[J].Bmc Medical Informatics & Decision Making,2014,14(1):1-18
[29] Li M H,Li J Z,Cheng S Y.Uncertain rule based method for evaluating data currency[J].Journal of Software,2014,25(Suppl.(2)):147-156(in Chinese) 李默涵,李建中,程思瑶.一种基于不确定规则的数据时效性判定方法[J].软件学报,2014,25(Suppl.(2)):147-156
[30] Yang C,Wu A R.Method of evaluation data freshness based on reduction-factor[J].Computer Engineering and Design,2010,31(3):684-686(in Chinese) 杨超,吴爱荣.基于衰减因子的评价数据时效性处理方法[J].计算机工程与设计,2010,31(3):684-686

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .