Product Recommendation
34 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
Latest papers
Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection.
Learning Neural Set Functions Under the Optimal Subset Oracle
Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery.
Context-aware Retail Product Recommendation with Regularized Gradient Boosting
A total of 167 participants participated in the challenge, and we secured the 6th rank during the final evaluation with an MRR of 0. 4658 on the test set.
Cross-Market Product Recommendation
hamedrab/forec
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We introduce and formalize the problem of cross-market product recommendation, i. e., market adaptation.
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation.
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.
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.
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.
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.
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.
