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Found 10 papers, 2 papers with code
Influence Maximization with Fairness at Scale (Extended Version)
In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes are fairly affected, i. e., are proportionally similar between the original network and the affected users.
SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data
Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources.
HyperionSolarNet: Solar Panel Detection from Aerial Images
The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power.
Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery
Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability.
Revenue Maximization of Airbnb Marketplace using Search Results
The initial output of the pricing strategy is a price vector for the top displayed items in one search event.
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming
In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service.
Predicting Audio Advertisement Quality
In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement.
Predicting Session Length in Media Streaming
Session length is a very important aspect in determining a user's satisfaction with a media streaming service.
Sequential ranking under random semi-bandit feedback
In many web applications, a recommendation is not a single item suggested to a user but a list of possibly interesting contents that may be ranked in some contexts.
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity.
