Zobrazeno 1 - 10
of 573
pro vyhledávání: '"Lalmas, Mounia"'
Slate recommendation is a technique commonly used on streaming platforms and e-commerce sites to present multiple items together. A significant challenge with slate recommendation is managing the complex combinatorial choice space. Traditional method
Externí odkaz:
http://arxiv.org/abs/2408.06883
Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but damage long-term user engagement. A possible solution to estimate the lo
Externí odkaz:
http://arxiv.org/abs/2404.15691
Autor:
Damianou, Andreas, Fabbri, Francesco, Gigioli, Paul, De Nadai, Marco, Wang, Alice, Palumbo, Enrico, Lalmas, Mounia
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in
Externí odkaz:
http://arxiv.org/abs/2403.07478
We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach employs a two-stage methodology combining representation learning and
Externí odkaz:
http://arxiv.org/abs/2403.00584
Autor:
Dupret, Georges, Sozinov, Konstantin, Gonzalez, Carmen Barcena, Zacks, Ziggy, Yuan, Amber, Carterette, Benjamin, Mai, Manuel, Bansal, Shubham, Liang, Gwo, Lien, Gatash, Andrey, Ojeda, Roberto Sanchis, Lalmas, Mounia
Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and service
Externí odkaz:
http://arxiv.org/abs/2403.00133
Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit
Externí odkaz:
http://arxiv.org/abs/2307.09943
The ability to answer causal questions is crucial in many domains, as causal inference allows one to understand the impact of interventions. In many applications, only a single intervention is possible at a given time. However, in some important area
Externí odkaz:
http://arxiv.org/abs/2210.05446
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. G
Externí odkaz:
http://arxiv.org/abs/2204.10463