Predicting l-CrossSold products using connected components: A clustering-based recommendation system
作者：
Highlights：
• We provide a novel data representation for the point-of-sale transactional datasets.
• We have developed a novel similarity measure that captures the product’s context.
• A novel algorithm to predict the cross-sold items.
• Our algorithm obtains the top N recommended items for an active user efficiently.
• Our algorithm retrieves the most frequent items with a significant performan.
摘要
•We provide a novel data representation for the point-of-sale transactional datasets.•We have developed a novel similarity measure that captures the product’s context.•A novel algorithm to predict the cross-sold items.•Our algorithm obtains the top N recommended items for an active user efficiently.•Our algorithm retrieves the most frequent items with a significant performan.
论文关键词：Recommendation systems,Cross-selling,Clustering analysis,Association mining,Speedup
论文评审过程：Received 3 April 2021, Revised 3 April 2022, Accepted 5 April 2022, Available online 22 April 2022, Version of Record 26 April 2022.
论文官网地址：https://doi.org/10.1016/j.elerap.2022.101148
