Product Recommendation
34 papers with code • 1 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Product Recommendation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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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
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.
Gated Attentive-Autoencoder for Content-Aware Recommendation
allenjack/GATE
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In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.
Adaptive Sequence Submodularity
In many machine learning applications, one needs to interactively select a sequence of items (e. g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e. g., guiding an agent through a series of states).
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
msn199959/Logo-2k-plus-Dataset
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Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification.
BERT Goes Shopping: Comparing Distributional Models for Product Representations
vinid/prodb
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ACL (ECNLP) 2021
Word embeddings (e. g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}.
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification
Extreme-classification/GalaXC
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The Web Conference 2021
An efficient end-to-end implementation of GalaXC is presented that could be trained on a dataset with 50M labels and 97M training documents in less than 100 hours on 4×V100 GPUs.
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Hence, the key is to make full use of rich interaction information among streamers, users, and products.
ECLARE: Extreme Classification with Label Graph Correlations
Extreme-classification/ECLARE
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This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
DECAF: Deep Extreme Classification with Label Features
Extreme-classification/DECAF
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This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
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.
