Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6861446 | Knowledge-Based Systems | 2018 | 23 Pages |
Abstract
In this research, we introduce a novel approach for a personal price aware multi-seller recommender system (PMSRS) which implicitly models a consumer's willingness to pay (WTP) for a specific product, taking into account discount indication and seller reputation, and incorporating it within a context-aware recommendation model to improve its effectiveness. We use six months of transactional data from eBay.com to test the proposed approach and prove its validity and effectiveness. Our results show that the proposed approach provides a good estimation of the consumer's WTP, and that incorporating the consumer's WTP and seller's reputation into a recommender system significantly improves its prediction accuracy (F-score improvements of 84% compared to a matrix factorization recommendation model which doesn't take into account the seller's reputation or consumer's WTP).
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Asnat Greenstein-Messica, Lior Rokach,