26 papers with code • 1 benchmarks • 6 datasets
These leaderboards are used to track progress in Product Recommendation
LibrariesUse these libraries to find Product Recommendation models and implementations
Most implemented papers
Retrieving Similar E-Commerce Images Using Deep Learning
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.
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications.
MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
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.
A Tutorial on Thompson Sampling
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.
Distributed Learning of Deep Neural Networks using Independent Subnet Training
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.
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications.
Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible, and has numerous applications such as product recommendation.
Low-Rank Factorization of Determinantal Point Processes for Recommendation
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.
Complete the Look: Scene-based Complementary Product Recommendation
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.