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Found 4 papers, 1 papers with code
Session-based Recommendations with Recurrent Neural Networks
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
Ranked #2 on
Recommendation Systems
on MovieLens 20M
(nDCG@10 (full corpus) metric)
General factorization framework for context-aware recommendations
We demonstrate the framework's potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets.
Context-aware recommendations from implicit data via scalable tensor factorization
Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case.
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS.
