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.