Hierarchical Gating Networks for Sequential Recommendation

By Chen Ma, Peng Kang, Xue Liu. Knowledge Discovery and Data Mining, 2019

411 citations recorded by the source corpus.

The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/7f29eb8863755cafa9aeef8dbc55105f1dd8c587

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. Xiang Wang
3. Wayne Xin Zhao
4. Ji-rong Wen
5. Meng Wang
6. Tat-seng Chua
7. Yongfeng Zhang
8. Yupeng Hou

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Yongfeng Zhang
6. Wayne Xin Zhao
7. Yi Tay
8. Kun Zhang

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Wayne Xin Zhao
5. Le Wu
6. Yongfeng Zhang
7. Yupeng Hou
8. Ji-rong Wen

Citations in the corpus

• S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (2020, methodology): “h uses the multi-head attention mechanism to recommend the next item. (7) BERT4Rec [23] uses a Cloze objective loss for sequential recommendation by the bidirectional self-attention mechanism. (8) HGN[13] is recently proposed and adopts hierarchical gating networks to capture long-term and short-ter”
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, neutral): “HGN [40] SIGKDD 2019 MovieLens, Amazon, Goodreads”
• SSE-PT: Sequential Recommendation Via Personalized Transformer (2020, methodology): “• HGN: hierarchical gating networks method to solve the sequential recommendation problem [23], which incorporates the user embeddings and gating networks for better personalization than the SASRec model.”
• Filter-enhanced MLP is All You Need for Sequential Recommendation (2022, methodology): “We compare our proposed approach with the following baseline methods: (1) PopRec ranks items according to the popularity measured by the number of interactions; (2) FM [41] characterizes the pairwise interactions between variables using factorized model; (3) AutoInt [46] utilizes the multihead self-”
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “HGN [38] adopts a hierarchical gating networks to learn user behaviors from the perspectives of both long and short terms.”
• Recommender Systems with Generative Retrieval (2023, background): “• HGN [24]: Hierarchical Gating Network (HGN) captures the long-term and short-term user interests via a new gating architecture.”
• Is ChatGPT a Good Recommender? A Preliminary Study (2023, methodology): “For sequential recommendation, we adopt Caser [51], HGN [35], GRU4Rec [21], BERT4Rec [49], FDSA [64], SASRec [24] and S 3 -Rec [68] as baselines for comparison.”
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023, background): “• HGN [52] utilizes hierarchical gating networks to capture both long-term and short-term user interests from historical behaviors.”
• Next-item Recommendation with Sequential Hypergraphs (2020, background): “good success in next-item recommendation which aims to predict a user’s next actions based on the sequential interactions in the past [22, 25, 30, 35, 37, 39, 44].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “hierarchical attention networks to learn better short term user preference with feature-level attention and item level attention [231].”