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.

🔥🔥🔥 Latest Advances on Large Recommendation Models

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

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

A repository listing important datasets for multimodal recommender systems

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

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

[KDD'25] Revisiting Self-attention for Cross-domain Sequential 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 is the official implementation of our paper Deep User Preference Gating Transfer for Cross-domain Recommendation (DUPGT-CDR).

Existing cross-domain recommendation methods rely on overlapping users and source interaction sequences to learn preference transfer function, leaving the rich structural information in pre-trained target-domain embeddings unexploited. In this paper, we propose COTA (Cluster Optimal Transport Alignment), a framework for cold-start CDR.

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.