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Found 3 papers, 3 papers with code
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
Exponential Machines
Modeling interactions between features improves the performance of machine learning solutions in many domains (e. g. recommender systems or sentiment analysis).
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.
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