Search Results for author:
Found 9 papers, 6 papers with code
Session-based Recommendations with Recurrent Neural Networks
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
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Recommendation Systems
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Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
The Effect of Third Party Implementations on Reproducibility
Reproducibility of recommender systems research has come under scrutiny during recent years.
Widespread Flaws in Offline Evaluation of Recommender Systems
Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, since the proprietary nature of production recommenders prevents independent validation of A/B test setups and verification of online results.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
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.
