Next-item Recommendation with Sequential Hypergraphs

By Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

313 citations recorded by the source corpus.

There is an increasing attention on next-item recommendation systems to infer the dynamic user preferences with sequential user interactions. While the semantics of an item can change over time and across users, the item correlations defined by user interactions in the short term can be distilled to capture such change, and help in uncovering the dynamic user preferences. Thus, we are motivated to develop a novel next-item recommendation framework empowered by sequential hypergraphs. Specifically, the framework: (i) adopts hypergraph to represent the short-term item correlations and applies multiple convolutional layers to capture multi-order connections in the hypergraph; (ii) models the connections between different time periods with a residual gating layer; and (iii) is equipped with a fusion layer to incorporate both the dynamic item embedding and short-term user intent to the representation of each interaction before feeding it into the self-attention layer for dynamic user modeling. Through experiments on datasets from the ecommerce sites Amazon and Etsy and the information sharing platform Goodreads, the proposed model can significantly outperform the state-of-the-art in predicting the next interesting item for each user.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5a2974a5eac4dfaa59b32fad9ef444cc18817f29

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Junliang Yu
5. Chao Huang
6. Xin Xia
7. Lianghao Xia
8. Meng Wang

In Information Systems

1. Hongzhi Yin
2. Xiangnan He
3. Xiang Wang
4. Junliang Yu
5. Chao Huang
6. Xin Xia
7. Meng Wang
8. Lianghao Xia

In Management Science and Operations Research

1. Xiang Wang
2. Meng Wang
3. Xiangnan He
4. Le Wu
5. Kun Zhang
6. Lianghao Xia
7. Jundong Li
8. Chao Huang

Citations in the corpus

• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023, background): “Hypergraph-based Collaborative Filtering: • HyRec (Wang et al., 2020a) makes use of hypergraph to encode multi-order information between users and items.”
• Hypergraph Contrastive Collaborative Filtering (2022, background): “It has achieved promising performance in various domains, such as visual data representation [5, 28], language data understanding [2, 31], graph representation learning [29, 51] and recommender systems [22, 38, 45, 46].”
• Graph Few-shot Class-incremental Learning (2021, methodology): “has been widely used in various applications, such as recommendation [42], anomaly detection [4], and text classification [5].”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, methodology): “Graph Neural Networks (GNNs) (Wu et al. 2020; Zhou et al. 2018) recently have drawn increasing attention and their applications in SBR also have shown promising re-sults (Wang et al. 2020b,c; Yuan et al. 2019; Chen and Wong 2020).”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “Besides, researchers proposed to leverage the advances of GNN based models for recommendation [247], [248], [249].”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “The most recent combinations are HyperRec [38] and DHCF [16], which borrow the strengths of hypergraph neural networks to model the short-term user preference for nextitem recommendation and the high-order correlations among users and items for general collaborative filtering, respectively.”