Product Recommendation
34 papers with code • 1 benchmarks • 8 datasets
Libraries
Use these libraries to find Product Recommendation models and implementationsDatasets
Latest papers with no code
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Due to limited user interactions for each product (i. e. item), the corresponding item embedding in the CTR model may not easily converge.
Fast Dual-Regularized Autoencoder for Sparse Biological Data
Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery.
A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model
In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales).
Detecting Violations of Differential Privacy for Quantum Algorithms
Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring.
Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality
To make accurate recommendations, it is crucial to infer both the product attributes and the product specific facial features such as skin conditions or tone.
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings.
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Using such reviews to offer suggestion services may reduce the effectiveness of those recommendations.
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces
The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face the curse of dimensionality.
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.