Context-Aware Residual Transformer (CART) is a kiosk recommendation system (CART) that utilizes self-supervised learning techniques tailored to kiosks in an offline retail environment and developed by a collaboration between NS Lab @ CUK and IIP Lab @ Gachon University based on pure PyTorch backend.

[KDD'25] Revisiting Self-attention for Cross-domain Sequential Recommendation

This project implements a cross-domain recommendation system using datasets from multiple domains (movies, music, and books). The goal is to leverage user interactions across these domains to improve recommendation accuracy.

🔥🔥🔥 Latest Advances on Large Recommendation Models

Multimodal Dataset and Benchmark for Multi-domain and Cross-domain Recommendation System

Paper List of Pre-trained Foundation Recommender Models

Multi-domain Recommendation with Adapter Tuning

A repository listing important datasets for multimodal recommender systems

Pre-training and Transfer learning papers for recommendation

papers of universal user representation learning for recommendation

AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation

Is ID embedding necessary for multimodal recommender system？

This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.

Enhanced recommendations through sentiment analysis on reviews and prioritized popular attractions based on keyword frequency, ensuring more personalized and relevant suggestions

[ICDE 2022]Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

The source code and dataset for the RecGURU paper (WSDM 2022)

Item Silk Road: Recommending Items from Information Domains to Social Users, SIGIR2017

Review-Aware Cross Domain Product Recommendation