21 papers with code • 1 benchmarks • 6 datasets
LibrariesUse these libraries to find Product Recommendation models and implementations
In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity.
Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, we compressed our deep ranking model to a single CNN by coupling activations from multiple intermediate layers along with the last layer.
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.
These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution.
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications.
In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.