Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang, Feiran; Wen, Yiping; Guo, Ting; Liu, Jianxun; Cao, Buqing

doi:10.1002/cpe.5565

Cited by 13 publications

(3 citation statements)

References 46 publications

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“…Association rule mining, a research hotspot in recent years, has been widely applied in the era of big data (e.g., cause analysis of traffic accidents [1], association analysis of weather forecasting [2], interest-based real-time news recommendation [3], recommendation of bank marketing plan [4], recommendation of e-commerce matching purchase and shopping basket analysis [5]). In particular, it has been used to realize the data-driven optimization of complex systems in various industries [6].…”

Section: Introductionmentioning

confidence: 99%

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Zhang

Zhu

et al. 2021

Applied Sciences

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When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

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Section: Introductionmentioning

confidence: 99%

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Zhang

Zhu

et al. 2021

Applied Sciences

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“…Compared with Hadoop's MapReduce, Spark's computing speed is 10-100 times faster [31]. Spark supports Java, Python and Scala languages, which is very convenient and easy to use [32]. In order to solve the shortcomings of MapReduce in interactive and iterative computation, Spark introduced RDD (Resilient Distributed Data Set), the core component of Spark [33], which is based on the distributed processing of multiple nodes in the Directed Acyclic Graph (DAG).…”

Section: Spark Parallel Computing Frameworkmentioning

confidence: 99%

Parallel Algorithm of Improved FunkSVD Based on Spark

2021

KSII TIIS

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In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

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“…The authors of [15][16][17][18] found that factors that may significantly influence customers' purchasing decisions include shelf space allocation and layout display. Personal or social factors may also influence the urge to purchase another product, a phenomenon that has been studied via Market Basket Analysis [19][20][21][22][23][24][25], which analyzes the correlation of products that customers are most likely to buy at the same time [26][27][28]. The management of shelf space was found to be a problem affecting consumers' purchasing behaviors; the effectiveness of shelf layouts between nearby supermarkets was compared to investigate how to increase profits and improve customer satisfaction [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

Kaewyotha

Songpan

2021

Applied Sciences

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Product layout significantly impacts consumer demand for purchases in supermarkets. Product shelf renovation is a crucial process that can increase supermarket efficiency. The development of a sequential pattern mining algorithm for investigating the correlation patterns of product layouts, solving the numerous problems of shelf design, and the development of an algorithm that considers in-store purchase and shelf profit data with the goal of improving supermarket efficiency, and consequently profitability, were the goals of this research. The authors of this research developed two types of algorithms to enhance efficiency and reach the goals. The first was a PrefixSpan algorithm, which was used to optimize sequential pattern mining, known as the PrefixSpan mining approach. The second was a new multi-objective design that considered the objective functions of profit volumes and closeness rating using the mutation-based harmony search (MBHS) optimization algorithm, which was used to evaluate the performance of the first algorithm based on the PrefixSpan algorithm. The experimental results demonstrated that the PrefixSpan algorithm can determine correlation rules more efficiently and accurately ascertain correlation rules better than any other algorithms used in the study. Additionally, the authors found that MBHS with a new multi-objective design can effectively find the product layout in supermarket solutions. Finally, the proposed product layout algorithm was found to lead to higher profit volumes and closeness ratings than traditional shelf layouts, as well as to be more efficient than other algorithms.

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Collaborative filtering and association rule mining‐based market basket recommendation on spark

Cited by 13 publications

References 46 publications

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Parallel Algorithm of Improved FunkSVD Based on Spark

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

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