reading_list

Week 1 (random-walk based graph representation learning):

DeepWalk: Online Learning of Social Representations (XQQ, KDD 2014)

LINE: Large-scale Information Network Embedding (YS, WWW 2015)

node2vec: Scalable Feature Learning for Networks (XWT, KDD 2016)

Week 2 (random-walk based graph representation learning works for recommendation):

Item2Vec: Neural Item Embedding for Collaborative Filtering (XQQ, Arxiv 2016)

BiNE: Bipartite Network Embedding (YS, SIGIR 2018)

Metapath2vec: Heterogeneous Network Embedding (XWT, KDD 2017)

Week 3 (BP algorithm)

Learning the BP algorithm and writing some codes

Week 4 (graph neural networks)

Graph Convolutional Matrix Completion (XQQ, KDD-workshop 2018)

Graph Convolutional Networks (YS, ICLR 2017)

Inductive Representation Learning on Large Graphs (XWT, NIPS 2017)

Week 5 (Pytorch Learning and Code Reading)

Code: https://github.com/tkipf/pygcn API document: https://pytorch.org/docs/stable/index.html

XQQ: Dataloader Explanation

XWT: Model and Training Explanation

YS: Model Evaluation and Metrics Explanation

Week 6 (Content-based Recommendation)

ANR: Aspect-Based Neural Recommender (XQQ, CIKM 2018)

Cross-Domain Recommendation: An Embedding and Mapping Approach (YS, IJCAI 2017)

CATN: Cross-Domain Recommendation for Cold-Start Users Via Aspect Transfer Network (XWT, SIGIR 2020)

Week 7 (Sequential Modeling)

Attention Is All You Need (XQQ, NIPS 2017)

SASRec: Self-Attentive Sequential Recommendation (YS, ICDM 2018)

Sequential Recommendation with Graph Neural Networks (XWT. SIGIR 2021)

Week 8 (Contrastive Learning)

Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search (XQQ, Arxiv 2023)

Bipartite Graph Embedding via Mutual Information Maximization (YS, WSDM 2021)

XSimGCL: Towards Extremely Simple Graph Contrastive Learning (XWT. TKDE 2022)