Paper List of Pre-trained Foundation Recommender Models

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

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

🔥🔥🔥 Latest Advances on Large Recommendation Models

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

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

Multi-domain Recommendation with Adapter Tuning

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

papers of universal user representation learning for recommendation

Pre-training and Transfer learning papers for recommendation

A repository listing important datasets for multimodal recommender systems

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

Review-Aware Cross Domain Product Recommendation

Is ID embedding necessary for multimodal recommender system？

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.

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

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.