Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode
Retail platforms have widely implemented recommender systems to provide personalized recommendations to consumers, influencing sales significantly. However, under the hybrid selling mode where platforms offer both their products and third-party sellers’ products, the profitability of a recommender s...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/19/4/175 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850102841762381824 |
|---|---|
| author | Wei Wang Xinyu Han Yuqing Ma Gang Li |
| author_facet | Wei Wang Xinyu Han Yuqing Ma Gang Li |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Retail platforms have widely implemented recommender systems to provide personalized recommendations to consumers, influencing sales significantly. However, under the hybrid selling mode where platforms offer both their products and third-party sellers’ products, the profitability of a recommender system and the optimal allocation of recommendations become critical considerations. This paper introduces a game-theoretic model to investigate these issues and unveil how a recommender system and its characteristics influence prices and profits. A key finding is that the recommender system increases prices and profits only if the commission rate is high and the system is profit-oriented or inaccurate. Surprisingly, higher recommendation accuracy does not always translate into higher profits; it is advantageous only in a consumer-oriented system. Moreover, the retail platform tends to allocate more recommendations to its own product than to the third-party seller’s product, a strategy known as self-preferencing. This strategy gives the platform a competitive edge and boosts its profit compared to the third-party seller. Furthermore, the degree of self-preferencing varies with the accuracy and orientation of the recommendation system. Specifically, in a consumer-oriented system, self-preferencing increases with accuracy, while in a profit-oriented system, it decreases with accuracy. |
| format | Article |
| id | doaj-art-8215085f2e7443d0ba4cf75a74804ec9 |
| institution | DOAJ |
| issn | 0718-1876 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj-art-8215085f2e7443d0ba4cf75a74804ec92025-08-20T02:39:40ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762024-12-011943606363110.3390/jtaer19040175Personalized Recommendation in a Retail Platform Under the Hybrid Selling ModeWei Wang0Xinyu Han1Yuqing Ma2Gang Li3School of Management, The Key Lab of the Ministry of Education for Process Management & Efficiency Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Management, The Key Lab of the Ministry of Education for Process Management & Efficiency Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Management, The Key Lab of the Ministry of Education for Process Management & Efficiency Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Management, The Key Lab of the Ministry of Education for Process Management & Efficiency Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaRetail platforms have widely implemented recommender systems to provide personalized recommendations to consumers, influencing sales significantly. However, under the hybrid selling mode where platforms offer both their products and third-party sellers’ products, the profitability of a recommender system and the optimal allocation of recommendations become critical considerations. This paper introduces a game-theoretic model to investigate these issues and unveil how a recommender system and its characteristics influence prices and profits. A key finding is that the recommender system increases prices and profits only if the commission rate is high and the system is profit-oriented or inaccurate. Surprisingly, higher recommendation accuracy does not always translate into higher profits; it is advantageous only in a consumer-oriented system. Moreover, the retail platform tends to allocate more recommendations to its own product than to the third-party seller’s product, a strategy known as self-preferencing. This strategy gives the platform a competitive edge and boosts its profit compared to the third-party seller. Furthermore, the degree of self-preferencing varies with the accuracy and orientation of the recommendation system. Specifically, in a consumer-oriented system, self-preferencing increases with accuracy, while in a profit-oriented system, it decreases with accuracy.https://www.mdpi.com/0718-1876/19/4/175recommender systemsretail platformselling modepersonalizationgame theory |
| spellingShingle | Wei Wang Xinyu Han Yuqing Ma Gang Li Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode Journal of Theoretical and Applied Electronic Commerce Research recommender systems retail platform selling mode personalization game theory |
| title | Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode |
| title_full | Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode |
| title_fullStr | Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode |
| title_full_unstemmed | Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode |
| title_short | Personalized Recommendation in a Retail Platform Under the Hybrid Selling Mode |
| title_sort | personalized recommendation in a retail platform under the hybrid selling mode |
| topic | recommender systems retail platform selling mode personalization game theory |
| url | https://www.mdpi.com/0718-1876/19/4/175 |
| work_keys_str_mv | AT weiwang personalizedrecommendationinaretailplatformunderthehybridsellingmode AT xinyuhan personalizedrecommendationinaretailplatformunderthehybridsellingmode AT yuqingma personalizedrecommendationinaretailplatformunderthehybridsellingmode AT gangli personalizedrecommendationinaretailplatformunderthehybridsellingmode |