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Found 13 papers, 3 papers with code
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
While promising, this move presents significant challenges for personalized recommendations.
Multi-Modal Trajectory Prediction of NBA Players
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game.
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.
Proceedings of the 2017 AdKDD & TargetAd Workshop
Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue.
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.
Network-Efficient Distributed Word2vec Training System for Large Vocabularies
Word2vec is a popular family of algorithms for unsupervised training of dense vector representations of words on large text corpuses.
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr
As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity.
E-commerce in Your Inbox: Product Recommendations at Scale
In recent years online advertising has become increasingly ubiquitous and effective.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.
Social and Information Networks Computers and Society
