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Found 10 papers, 3 papers with code
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
We highlight (1) neural networks' ability to handle large training data size, user- and item-embeddings allows for more accurate models than GBDTs in this setting, and (2) because GBDTs are less reliant on specialised hardware, they can provide an equally accurate model at a lower cost.
Ad-load Balancing via Off-policy Learning in a Content Marketplace
We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue.
Task Preferences across Languages on Community Question Answering Platforms
With the steady emergence of community question answering (CQA) platforms like Quora, StackExchange, and WikiHow, users now have an unprecedented access to information on various kind of queries and tasks.
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
We address this challenge by aiming to learn the effect of a single-intervention from both observational data and sets of interventions.
Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing
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.
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner.
The Music Streaming Sessions Dataset
In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of 160 million listening sessions and associated user actions.
Towards Task Understanding in Visual Settings
We consider the problem of understanding real world tasks depicted in visual images.
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks.
