Product Recommendation
40 papers with code • 1 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Product Recommendation
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Libraries
Use these libraries to find Product Recommendation models and implementationsDatasets
EXTREME CLASSIFICATION
Logo-2K+
VISUELLE2.0
OTTO Recommender Systems Dataset
LSEC
Most implemented papers
Representation Learning for Attributed Multiplex Heterogeneous Network
cenyk1230/GATNE
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Network embedding (or graph embedding) has been widely used in many real-world applications.
Retrieving Similar E-Commerce Images Using Deep Learning
gofynd/mildnet
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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.
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.
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.
MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
gofynd/mildnet
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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.
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.
Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
In this paper, we study how to construct effective explainable product search by comparing model-agnostic explanation paradigms with model-intrinsic paradigms and analyzing the important factors that determine the performance of product search explanations.
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.
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.
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
appier/compatibility-family-learning
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Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible.
