A Mechanism for Optimizing Media Recommender Systems
- URL: http://arxiv.org/abs/2406.16212v2
- Date: Mon, 22 Jul 2024 17:20:14 GMT
- Title: A Mechanism for Optimizing Media Recommender Systems
- Authors: Brian McFadden,
- Abstract summary: An optimal solution can be achieved when the media source considers the impact of overreach in a cost function.
A practical algorithm for generating the optimal distribution for each consumer is provided.
- Score: 0.0
- License:
- Abstract: 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.
Related papers
- Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels.
We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
arXiv Detail & Related papers (2024-10-09T04:20:15Z) - Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation [55.75188191403343]
We introduce utility, which is a function predefined by each user and describes the trade-off between cost and performance of BO.
We validate our algorithm on various LC datasets and found it outperform all the previous multi-fidelity BO and transfer-BO baselines we consider.
arXiv Detail & Related papers (2024-05-28T07:38:39Z) - User Welfare Optimization in Recommender Systems with Competing Content Creators [65.25721571688369]
In this study, we perform system-side user welfare optimization under a competitive game setting among content creators.
We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content.
These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies.
arXiv Detail & Related papers (2024-04-28T21:09:52Z) - A Personalized Framework for Consumer and Producer Group Fairness
Optimization in Recommender Systems [13.89038866451741]
We propose CP-FairRank, an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side.
We demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising overall recommendation quality.
arXiv Detail & Related papers (2024-02-01T10:42:05Z) - Interactive Hyperparameter Optimization in Multi-Objective Problems via
Preference Learning [65.51668094117802]
We propose a human-centered interactive HPO approach tailored towards multi-objective machine learning (ML)
Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator.
arXiv Detail & Related papers (2023-09-07T09:22:05Z) - CPFair: Personalized Consumer and Producer Fairness Re-ranking for
Recommender Systems [5.145741425164946]
We present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side.
We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality.
arXiv Detail & Related papers (2022-04-17T20:38:02Z) - Exploring Customer Price Preference and Product Profit Role in
Recommender Systems [0.4724825031148411]
We show the impact of manipulating profit awareness of a recommender system.
We propose an adjustment of a predicted ranking for score-based recommender systems.
In the experiments, we show the ability to improve both the precision and the generated recommendations' profit.
arXiv Detail & Related papers (2022-03-13T12:08:06Z) - Optimizer Amalgamation [124.33523126363728]
We are motivated to study a new problem named Amalgamation: how can we best combine a pool of "teacher" amalgamations into a single "student" that can have stronger problem-specific performance?
First, we define three differentiable mechanisms to amalgamate a pool of analyticals by gradient descent.
In order to reduce variance of the process, we also explore methods to stabilize the process by perturbing the target.
arXiv Detail & Related papers (2022-03-12T16:07:57Z) - Sequential Information Design: Markov Persuasion Process and Its
Efficient Reinforcement Learning [156.5667417159582]
This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs)
Planning in MPPs faces the unique challenge in finding a signaling policy that is simultaneously persuasive to the myopic receivers and inducing the optimal long-term cumulative utilities of the sender.
We design a provably efficient no-regret learning algorithm, the Optimism-Pessimism Principle for Persuasion Process (OP4), which features a novel combination of both optimism and pessimism principles.
arXiv Detail & Related papers (2022-02-22T05:41:43Z) - Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation [3.0938904602244355]
Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains.
Attempts to increase aggregate diversity often result in lower recommendation accuracy for end users.
We propose a two-sided post-processing approach in which both user and item utilities are considered.
arXiv Detail & Related papers (2020-06-05T22:12:25Z) - Heterogeneous Causal Learning for Effectiveness Optimization in User
Marketing [2.752817022620644]
We propose a treatment effect optimization methodology for user marketing.
This algorithm learns from past experiments and utilizes novel optimization methods to optimize cost efficiency with respect to user selection.
Our proposed constrained and direct optimization algorithms outperform by 24.6% compared with the best performing method in prior art and baseline methods.
arXiv Detail & Related papers (2020-04-21T01:34:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.