Welfare-Optimized Recommender Systems
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866914954935795712 |
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| author | Heymann, Benjamin Vasile, Flavian Rohde, David |
| author_facet | Heymann, Benjamin Vasile, Flavian Rohde, David |
| contents | We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness set. Notably, the price information and the shopper's Willingness-To-Pay play crucial roles. Furthermore, to better account for the commercial nature of the recommendation, we unify the retailer and shoppers' contradictory objectives into a single welfare metric, which we propose as a new recommendation goal. We test our framework on synthetic data and show its performance in a wide range of scenarios. This new framework, that was absent from the Recommender System literature, opens the door to Welfare-Optimized Recommender Systems, couponing, and price optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_13845 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Welfare-Optimized Recommender Systems Heymann, Benjamin Vasile, Flavian Rohde, David Computer Science and Game Theory Optimization and Control We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness set. Notably, the price information and the shopper's Willingness-To-Pay play crucial roles. Furthermore, to better account for the commercial nature of the recommendation, we unify the retailer and shoppers' contradictory objectives into a single welfare metric, which we propose as a new recommendation goal. We test our framework on synthetic data and show its performance in a wide range of scenarios. This new framework, that was absent from the Recommender System literature, opens the door to Welfare-Optimized Recommender Systems, couponing, and price optimization. |
| title | Welfare-Optimized Recommender Systems |
| topic | Computer Science and Game Theory Optimization and Control |
| url | https://arxiv.org/abs/2206.13845 |