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Found 6 papers, 1 papers with code
Semi-supervised Adversarial Learning for Complementary Item Recommendation
In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i. e., for items for which no co-purchase statistics yet exist.
Collaborative Image Understanding
A series of experiments on datasets from e-commerce and social media demonstrates that considering collaborative signals helps to significantly improve the performance of the main task of image classification by up to 9. 1%.
A Black-Box Attack Model for Visually-Aware Recommender Systems
Due to the advances in deep learning, visually-aware recommender systems (RS) have recently attracted increased research interest.
Understanding Convolutional Neural Networks for Text Classification
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.
Rank and Rate: Multi-task Learning for Recommender Systems
The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks.
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations
Latent factor models for recommender systems represent users and items as low dimensional vectors.
