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.
SummaryRecommender systems are becoming increasingly critical to the success of commerce sales. In spite of their benefits, they suffer from some major challenges including recommendation quality such as the accuracy, diversity, and novelty of recommendations. In the context of retail business, the novelty of recommendations is of especial importance because it can directly affect customers' probabilities of buying commodity and whether to visit stores again. However, tradition algorithms for retail commodity recommendation never consider the problem of improving the novelty of recommendations. To address this, a novel multiarmed bandit and gradient boosting decision tree‐based retail commodity recommendation approach is proposed in this article, which is named MGRCR. It can increase recommendations' novelty while maintaining comparable levels of in the context of retailing. The effectiveness of our proposed approach has been proved by comprehensive experiments with real‐world commerce datasets and different state‐of‐the‐art recommendation techniques.
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