crystal22's repositories
CAPRI
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
CSLSL
PyTorch Implementation of CSLSL
RecGURU
The source code and dataset for the RecGURU paper (WSDM 2022)
ETL-master
This is a pytorch implementation of our paper: "Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation"
SIMS
[TNSE 2021] Modeling User Interests With Online Social Network Influence by Memory Augmented Sequence Learning
Time-Aware-Location-Prediction
[TKDE 2022] Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM
Bigscity-LibCity-Docs
Docs of LibCity
WSDM2022-PTUPCDR
This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.
ASGNN
Attentive Sequential Model Based on Graph Neural Network for Next POI Recommendation
pseudoscience-paper
Code for "It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations
Seq2Graph
Implemention of Seq2Graph architecture from paper by Xuan-Hong Dang, et al. in Pytorch
poi-type-prediction
This code implements multimodal models for point-of-interest type prediction using text and images.
DisenCDR
[SIGIR 2022]DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation
TLR-M
In this research, we present a problem of queuing time aware next POI recommendation and demonstrate how it is non-trivial to both recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi head attention transformer model called TLR-M. The model recommends next POIs to the target users and predicts queuing time to access the POIs simultaneously. By utilizing multi-head attention, the TLR-M model can integrate long range dependencies between any two POI visit efficiently and evaluate their contribution to select next POIs and to predict queuing time. To use this code in your research work please cite the following paper. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. Transformer-based multi-task learning for queuing time aware next poi recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 510–523. Springer, 2021, DOI: https://doi.org/10.1007/978-3-030-75765-6_41
STARec
Code for WWW'22 "Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data"
SKGEM
A Scalable Knowledge Graph Embedding Model for Next Point-of-Interest Recommendation
STiSAN.pytorch
A Spatio-Temporal Interval-Aware Sequential POI Recommendation
awesome-multi-modal-recommendation
A curated list of awesome multi-modal recommendation.