Name
Title | ||
|---|---|---|
Collaborative filtering and association rule mining‐based market basket recommendation on spark |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/cpe.5565 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | Field | DocType |
association rule mining,basket analysis,collaborative filtering,recommendation,spark | Data mining,Market basket,Collaborative filtering,Spark (mathematics),Computer science,Association rule learning,Distributed computing | Journal |
Volume | Issue | ISSN |
32.0 | 7.0 | 1532-0626 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feiran Wang | 1 | 2 | 2.08 |
| Yiping Wen | 2 | 25 | 8.59 |
| Tianhang Guo | 3 | 1 | 0.36 |
| Jianxun Liu | 4 | 640 | 67.12 |
| Buqing Cao | 5 | 9 | 5.93 |