Lifelong sequential modeling for user response prediction. A comprehensive evaluation framework for our SIGIR 2019 paper.

A list of large language models for user modeling (LLM-UM) papers, based on "User Modeling in the Era of Large Language Models: Current Research and Future Directions" at DEBULL

A curated list of papers on cold-start recommendations.

Simulator for training and evaluation of Recommender Systems

The Excite-O-Meter is a user-friendly Unity plugin for XR creators to integrate physiological user information in the development and evaluation of XR content. Specifically, it allows to record, analyze, and visualize validated metrics of heart activity from the chest strap sensor Polar H10 and movement trajectories from VR headsets.

cross-domain recommendation，transfer learning，pre-training，self-supervise learning papers and datasets

User-Centric Conversational Recommendation with Multi-Aspect User Modeling (UCCR)

[WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation".

papers of universal user representation learning for recommendation

AMBAL-based NILM Trace generator

Pre-training and Transfer learning papers for recommendation

SCoRe is a sequential recommendation model with dual side neighbor-based collaborative filtering. Implementation of our WSDM 2020 paper.

DataSets links for recommender systems research, in particular for transfer learning, user representation, pre-training,lifelong learning, cold start recommendation

The official PyTorch implementation of "Learning to Simulate Daily Activities via Modeling Dynamic Human Needs" (WWW'23)

Implementation of "Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps"

迁移学习，预训练，表征学习，跨域推荐，冷启动，用户画像

Our CIKM21 Paper "Incorporating Query Reformulating Behavior into Web Search Evaluation"

Predicting Anchor Links between Heterogeneous Social Networks (ASONAM 2016)

UserBERT PyTorch implementation

learning universal user representation with lifelong learning mechanism for recommender systems