Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221000076-6.doi: 10.11896/jsjkx.221000076

• Big Data & Data Science • Previous Articles     Next Articles

Next-basket Recommendation Algorithm Based on Correlation Between Items Collaborative Filtering

JIANG Binze, DENG Xin, DU Yulu, ZHANG Heng   

  1. College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Published:2023-11-09
  • About author:JIANG Binze,born in 1997,postgra-duate.His main research interests include data mining and recommender systems.
    DU Yulu,born in 1987,Ph.D.His main research interests include information retrieval,intelligent information processing and recommender systems.
  • Supported by:
    Natural Science Foundation of Chongqing,China(cstc2020jcyj-msxmX0284) and Science and Technology Research Project of Chongqing Education Commission(KJQN202000625).

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Abstract: The next-basket recommendation system aims to recommend items that could be seen in their next-basket,based on the sequence of users’ historical baskets.However,the existing methods focus on the recommendation of each item in the shopping basket as an independent part,ignoring the relationship between items in the shopping basket,which impacts on recommendation accuracy.To solve this problem,a next-basket recommendation algorithm based on correlation between items collaborative filtering(CBICF) is proposed.Firstly,the historical shopping basket sequence of users is modeled to generate users’ personalized item frequency information,which is used for user’s nearest neighbor clustering.Then,item correlation matrix is generated by correlation between items measurement method,and the associated item information of the users’ preference items is obtained by weighted fusion method,to improve the accuracy of recommendations.Experimental comparison and analysis on two real data sets reveal that the proposed algorithm is superior to the comparison algorithm in indicators.Especially in the case of exploring new items,the accuracy of recommending is significantly improved compared with other methods based on collaborative filtering.

Key words: Next-basket recommendation, Collaborative filtering, Clustering, Correlation between items, Personalized item frequency information

CLC Number: 

  • TP391
[1]RESNICK P,VARIAN H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[2]VERBERT K,MANOUSELIS N,OCHOA X,et al.Context-aware recommender systems for learning:a survey and future challenges[J].IEEE Transactions on Learning Technologies,2012,5(4):318-335.
[3]HU H,HE X N,GAO J,et al.Modeling personalized item fre-quency information for next-basket recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2020:1071-1080.
[4]FAGGIOLI G,POLATO M,AIOLLI F.Recency aware collaborative filtering for next basket recommendation[C]// Procee-dings of the 28th ACM Conference on User Modeling.2020:80-87.
[5]QIN Y Q.WANG P F,LI C L.The World is Binary:ContrastiveLearning for Denoising Next Basket Recommendation[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:859-868.
[6]KAYA T,KALELI C.A novel top-n recommendation methodfor multi-criteria collaborative filtering[J].Expert Systems with Applications,2022,198:116695.
[7]CREMONESI P,KOREN Y,TURRIN R.Performance of re-commender algorithms on top-n recommendation tasks[C]// Proceedings of the Fourth ACM Conference on Recommender systems.2010:39-46.
[8]LI J C,WANG Y J,MCAULEY J.Time interval aware self-attention for sequential recommendation[C]// Proceedings of the 13th International Conference on Web Search and Data Mining.2020:322-330.
[9]KANG W C,MCAULEY J.Self-attentive sequential recom-mendation[C]// Proceedings of the 2018 IEEE International Conference on Data Mining(ICDM).IEEE,2018:197-206.
[10]TANG J X,WANG K.Personalized top-n sequential recommendation via convolutional sequence embedding[C]// Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:565-573.
[11]YU L,SUN L L,DU B W,et al.Predicting temporal sets with deep neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2020:1083-1091.
[12]DUC-TRONG L,HADY W L,FANG Y,et al.Correlation-sensitive next-basket recommendation[C]// Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.2019:2808-2814.
[13]RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized markovchains for next-basket recommendation[C]// Proceedings of the 19th International Confe-rence on World Wide Web.2010:811-820.
[14]WANG P F,GUO J F,LAN Y Y,et al.Learning hierarchicalrepresentation model for nextbasket recommendation[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.2015:403-412.
[15]YU F,LIU Q,WU S,et al.A dynamic recurrent model for next basket recommendation[C]// Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:729-732.
[16]WANG W Y.Research on Next Basket Recommendation Algorithm[J].Computer & Digital Engineering,2021,49(2):340-346.
[17] YANG X W,GUO Y,LIU Y,et al.A survey of collaborative filtering based social recommender systems[J].Computer communications,2014,41:1-10.
[18]ZHAO D D,HU X Y,XIONG S W,et al.K-means clustering and kNN classification based on negative databases[J].Applied Soft Computing,2021,110:107732.
[19]WANG D W,YIH Y,VENTRESCA M.Improving neighbor-based collaborative filtering by using a hybrid similarity measurement[J].Expert Systems with Applications,2020,160:113651.
[20]LEE Y H,WEI C P,CHENG T H,et al.Nearest-neighbor-based approach to time-series classification[J].Decision Support Systems,2012,53(1):207-217.
[21]HAGEN S,HANK L,HASIM S.Neural speech recognizer:Acoustic-to-word LSTM model for large vocabulary speech re-cognition[J].arXiv:1610.09975,2016.
[22]ORIOL V,ALEXANDER T,SMAY B,et al.Show and tell:A neural image caption generator[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3156-3164.
[23]NI W J,GUO H Y,LIU T,et al.Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks[J].Data Analysis and Knowledge Discovery,2020,4(2/3):68-77.
[24]ARIANNEZHAD M,JULLIEN S,LI M,et al.ReCANet:A Repeat Consumption-Aware Neural Network for Next Basket Re-commendation in Grocery Shopping[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.2022.
[25]HU H J,HE X N.Sets2sets:Learning from sequential sets with neural networks[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2019:1491-1499.
[26]ZHOU Y T,CHU H,YANG W Y,et al.Time Perception and Collaborative Mining for Next-Basket Recom-mendation[J].Journal of Wuhan University(Science Edition),2021,67(6):525-531.
[27]HUANG Z H,ZHANG J W,TIAN C Q,et al.Survey on learning-to-rank based recommendation algorithms [J].Journal of Software,2016,27(3):691-713.
[28]MERTON R K.The Matthew effect in science:The reward and communication systems of science are considered[J].Science,1968,159(3810):56-63.
[29]LI M,JULLIEN S,ARIANNEZHAD M,et al.A Next Basket Recommendation Reality Check[J].arXiv:2109.14233,2021.
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