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Movie Recommender Systems: Implementation and Performance Evaluation
Over the years, explosive growth in the number of items in the catalog o...
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Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by m...
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A Collaborative Filtering Approah for the Automatic Tuning of Compiler Optimisations
Selecting the right compiler optimisations has a severe impact on progra...
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A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations
Selecting the right compiler optimisations has a severe impact on progra...
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A Collaborative Filtering Recommender System for Test Case Prioritization in Web Applications
The use of relevant metrics of software systems could improve various so...
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Deep Learning feature selection to unhide demographic recommender systems factors
Extracting demographic features from hidden factors is an innovative con...
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Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Hybrid methods that utilize both content and rating information are comm...
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Modurec: Recommender Systems with Feature and Time Modulation
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time). To address these problems, we propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism, which has demonstrated its effectiveness in several fields. While time information helps mitigate the effects of concept shift, the combination of user and item features improve prediction performance when little data is available. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings compared with standard autoencoder-based methods and other collaborative filtering approaches.
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