Name
Abstract | ||
|---|---|---|
Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing schemes. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DSAA49011.2020.00018 | 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | DocType | ISSN |
utility mining,generalized association rules,retail management,product taxonomy | Conference | 2472-1573 |
ISBN | Citations | PageRank |
978-1-7281-8207-0 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chinmay Bapna | 1 | 0 | 0.34 |
| P. Krishna Reddy | 2 | 105 | 17.26 |
| Anirban Mondal | 3 | 386 | 31.29 |