Shifting Consumption towards Diverse Content on Music Streaming Platforms

Hansen, Christian; Mehrotra, Rishabh; Hansen, Casper Worm; Brost, Brian M.; Maystre, Lucas; Lalmas, Mounia

doi:10.1145/3437963.3441775

Cited by 30 publications

(13 citation statements)

References 67 publications

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“…Improvements in system design and enhanced data collection may produce better results even if the core algorithms are unchanged. Another design strategy is to differentiate between the contexts and their associated user expectations, to identify places in the application environment where users may be more amenable to exposure to diverse content (Hansen et al 2021;Tomasi et al 2020).…”

Section: Discussionmentioning

confidence: 99%

The multisided complexity of fairness in recommender systems

et al. 2022

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Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area.

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Section: Discussionmentioning

confidence: 99%

The multisided complexity of fairness in recommender systems

et al. 2022

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“…Multi-objective recommendations. Multi-objective optimisation is a well-studied area in operation research and machine learning [40], with recent approaches often aggregating multiple objectives into a single function [21,28], or designing reward functions with multiple objectives [16]. Several search and recommendation applications need to meet multi-objective requirements, from click shaping [2] to email volume optimisation [15].…”

Section: Related Workmentioning

confidence: 99%

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Bugliarello

Mehrotra²,

Lalmas³

2022

Proceedings of the ACM Web Conference 2022

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We consider the task of sequencing tracks on music streaming platforms where the goal is to maximise not only user satisfaction, but also artist-and platform-centric objectives, needed to ensure long-term health and sustainability of the platform. Grounding the work across four objectives: Sat, Discovery, Exposure and Boost, we highlight the need and the potential to trade-off performance across these objectives, and propose Mostra, a Set Transformerbased encoder-decoder architecture equipped with submodular multi-objective beam search decoding. The proposed model affords system designers the power to balance multiple goals, and dynamically control the impact on one objective to satisfy other objectives. Through extensive experiments on data from a large-scale music streaming platform, we present insights on the trade-offs that exist across different objectives, and demonstrate that the proposed framework leads to a superior, just-in-time balancing across the various metrics of interest. CCS CONCEPTS• Information systems → Recommender systems.

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“…In the RL setting, Zheng et al [46] focused on explorationexploitation strategies for promoting diversity, by randomly choosing random item candidates in the neighborhood of the current recommended item. Hansen et al [10] proposed a RL samplingbased ranker that produces a ranked list of diverse items. This model is a simple ranker and the model itself doesn't learn to produce diverse set of items, while the learning process utilizes the REINFORCE algorithm [41] which is known to suffer from highvariance.…”

Section: Related Workmentioning

confidence: 99%

Choosing the Best of Both Worlds

Stamenković

Karatzoglou

Arapakis

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one pays a significant price in terms of other important metrics: users get stuck in a "filter bubble" and their array of options is significantly reduced, hence degrading the quality of the user experience and leading to churn. Recommendation, and in particular session-based/sequential recommendation, is a complex task with multiple -and often conflicting objectivesthat existing state-of-the-art approaches fail to address.In this work, we take on the aforementioned challenge and introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, a novel Reinforcement Learning (RL) framework that can effectively address multi-objective recommendation tasks. The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. We integrate this framework with four stateof-the-art session-based recommendation models and compare it with a single-objective RL agent that only focuses on accuracy. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.

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Shifting Consumption towards Diverse Content on Music Streaming Platforms

Cited by 30 publications

References 67 publications

The multisided complexity of fairness in recommender systems

The multisided complexity of fairness in recommender systems

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Choosing the Best of Both Worlds

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