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Found 16 papers, 5 papers with code
Hybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
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Recommendation Systems
on Douban
Modulating early visual processing by language
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected.
Weakly-Supervised Conditional Embedding for Referred Visual Search
This paper introduces a new challenge for image similarity search in the context of fashion, addressing the inherent ambiguity in this domain stemming from complex images.
EdiBERT, a generative model for image editing
Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks.
Visual Reasoning with Multi-hop Feature Modulation
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
Multi-Task Determinantal Point Processes for Recommendation
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity.
Bandits Warm-up Cold Recommender Systems
Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets.
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
In general the evaluation of a RS is a critical issue.
Reducing statistical time-series problems to binary classification
The algorithms that we construct for solving these problems are based on a new metric between time-series distributions, which can be evaluated using binary classification methods.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon.
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning
A common assumption in recommender systems (RS) is the existence of a best fixed recommendation strategy.
Distributionally Robust Reinforcement Learning
Our formulation results in a efficient algorithm that accounts for a simple re-weighting of policy actions in the standard policy iteration scheme.
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
This task requires to fit an in-shop cloth image on the image of a person.
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
This task requires fitting an in-shop cloth image on the image of a person, which is highly challenging because it involves cloth warping, image compositing, and synthesizing.
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry.
Unveiling the Latent Space Geometry of Push-Forward Generative Models
We investigate the relationship between the performance of these models and the geometry of their latent space.
