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 |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Product Recommendation models and implementationsDatasets
EXTREME CLASSIFICATION
Logo-2K+
VISUELLE2.0
OTTO Recommender Systems Dataset
LSEC
Latest papers with no code
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years.
Improving Recommendation Systems with User Personality Inferred from Product Reviews
Experiments on our two newly contributed personality datasets -- Amazon-beauty and Amazon-music -- validate our hypothesis, showing performance boosts of 3--28%. Our analysis uncovers that varying personality types contribute differently to recommendation performance: open and extroverted personalities are most helpful in music recommendation, while a conscientious personality is most helpful in beauty product recommendation.
Learning-To-Embed: Adopting Transformer based models for E-commerce Products Representation Learning
For both the tasks, we collect an evaluation data from the fashion e-commerce platform and observe that XLNET model outperform other variants with a MRR of 0. 5 for NPR and NDCG of 0. 634 for CR.
Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics
The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation.
Recommending Related Products Using Graph Neural Networks in Directed Graphs
To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph.
Towards Correlated Sequential Rules
To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns.
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning
User activities in a session can be classified into two groups: Known Intent and Unknown intent.
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
To explain this generalization failure, we consider an intervention-based importance metric, which shows that a fine-tuned model captures spurious correlations and fails to learn the causal features that determine the relevance between any two text inputs.
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.
Automatic Facial Skin Feature Detection for Everyone
We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild.
