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Flavian Vasile
Researcher at Iowa State University
Publications - 53
Citations - 1168
Flavian Vasile is an academic researcher from Iowa State University. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 11, co-authored 47 publications receiving 768 citations. Previous affiliations of Flavian Vasile include Yahoo!.
Papers
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Proceedings ArticleDOI
Causal embeddings for recommendation
Stephen Bonner,Flavian Vasile +1 more
TL;DR: In this article, the authors propose a domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure, which is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy.
Proceedings ArticleDOI
Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation
TL;DR: Meta-Prod2vec as mentioned in this paper leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items and injects item metadata into the model as side information to regularize the item embedding.
Proceedings ArticleDOI
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Elena Smirnova,Flavian Vasile +1 more
TL;DR: A new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and parametrizing the hidden unit transitions as a function of context information is proposed.
Posted Content
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
TL;DR: RecoGym is introduced, an RL environment for recommendation, which is defined by a model of user traffic patterns on e-commerce and the users response to recommendations on the publisher websites, that could open up an avenue of collaboration between the recommender systems and reinforcement learning communities and lead to better alignment between offline and online performance metrics.
Proceedings ArticleDOI
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
TL;DR: This work proposes Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata and shows that the new item representations lead to better performance on recommendation tasks on an open music dataset.