# G3SR: Global Graph Guided Session-based Recommendation

arXiv: https://arxiv.org/abs/2203.06467 · HTML: https://mlsanity.com/paper/2203.06467/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 69 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification](https://mlsanity.com/paper/1802.08888/) (2018)
- [Interpretable and Efficient Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2005.13183/) (2020)
- [Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1911.11942/) (2019)
- [Interest-aware Message-Passing GCN for Recommendation](https://mlsanity.com/paper/2102.10044/) (2021)
- [Pre-trained Language Model Representations for Language Generation](https://mlsanity.com/paper/1903.09722/) (2019)
- [Using Temporal Data for Making Recommendations](https://mlsanity.com/paper/1301.2320/) (2013)
- [Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2107.00852/) (2021)
- [IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation](https://mlsanity.com/paper/1907.12377/) (2019)
- [DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System](https://mlsanity.com/paper/1901.04704/) (2019)
- [RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems](https://mlsanity.com/paper/1903.10794/) (2019)

## Built on by (most cited first, 2 of 2)

- [AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing](https://mlsanity.com/paper/2304.07014/) (2023)
- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)

## Abstract (arXiv)

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named G3SR (Global Graph Guided Session-based Recommendation). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.