A Mechanism for Optimizing Media Recommender Systems
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916331830378496 |
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| author | McFadden, Brian |
| author_facet | McFadden, Brian |
| contents | A mechanism is described that addresses the fundamental trade off between media producers who want to increase reach and consumers who provide attention based on the rate of utility received, and where overreach negatively impacts that rate. An optimal solution can be achieved when the media source considers the impact of overreach in a cost function used in determining the optimal distribution of content to maximize individual consumer utility and participation. The result is a Nash equilibrium between producer and consumer that is also Pareto efficient. Comparison with the literature on Recommender systems highlights the advantages of the mechanism, including identifying an optimal content volume for the consumer and improvements for optimizing with multiple objectives. A practical algorithm for generating the optimal distribution for each consumer is provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Mechanism for Optimizing Media Recommender Systems McFadden, Brian Theoretical Economics Computer Science and Game Theory Information Retrieval H.3.3; F.m A mechanism is described that addresses the fundamental trade off between media producers who want to increase reach and consumers who provide attention based on the rate of utility received, and where overreach negatively impacts that rate. An optimal solution can be achieved when the media source considers the impact of overreach in a cost function used in determining the optimal distribution of content to maximize individual consumer utility and participation. The result is a Nash equilibrium between producer and consumer that is also Pareto efficient. Comparison with the literature on Recommender systems highlights the advantages of the mechanism, including identifying an optimal content volume for the consumer and improvements for optimizing with multiple objectives. A practical algorithm for generating the optimal distribution for each consumer is provided. |
| title | A Mechanism for Optimizing Media Recommender Systems |
| topic | Theoretical Economics Computer Science and Game Theory Information Retrieval H.3.3; F.m |
| url | https://arxiv.org/abs/2406.16212 |