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Collaborative filtering and association rule mining‐based market basket recommendation on spark
Author(s) -
Wang Feiran,
Wen Yiping,
Guo Tianhang,
Liu Jianxun,
Cao Buqing
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5565
Subject(s) - affinity analysis , association rule learning , collaborative filtering , spark (programming language) , computer science , market basket , big data , recommender system , data mining , map reduce , commodity , machine learning , finance , business , macroeconomics , economics , programming language
Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.