Sequential Recommender System based on Hierarchical Attention Networks

By Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, Jian Wu. International Joint Conference on Artificial Intelligence, 2018

423 citations recorded by the source corpus.

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.

Subfields: Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/0b42168f22713c48d8b0ce3bc8f680d347184d88

How researchers see this paper

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

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Yi Tay
7. Jiacheng Li
8. Tat-seng Chua

In Transportation

1. Xiangnan He
2. Xiang Wang
3. Jiacheng Li
4. Meng Wang
5. Yi Tay
6. Jie Lu
7. Shuai Zhang
8. Quan Z. Sheng

Citations in the corpus

• Time Interval Aware Self-Attention for Sequential Recommendation (2020, background): “SHAN [33] models the previous several items and a long history of a user via a two-layer attention mechanism to obtain the short-and long-term preferences of a user.”
• Attention in Natural Language Processing (2019, background): “recommendation [22], [23], time-series analysis [24], [25], games [26], and mathematical problems [27], [28].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “…[225], GCE-GNN [226],SGNN-HN [227], [228] Temporal and Sequential Models Hierarchical attention networks with long and short term interest SHAN [229],HRM [230],HGN [231], MARank [232],Fissa [233],SSE-PT [234] RNN based models RRN [235],BINN [204],HIERNN [236] Attention based models for user…”
• Artificial intelligence in recommender systems (2020, background): “Further, the two kinds of preferences are fine-tuned by a hierarchical attention network [101].”
• Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation (2021, methodology): “Hierarchical attention network [29] is used for capturing both long-term and short-term sequence information.”
• A Survey on Session-based Recommender Systems (2019, background): “Cannot capture sequential information [35],[59],[65],[69],[125],[127], [145],[152]”
• Deep Learning Based Recommender System (2017, methodology): “Attention-based CNNs are capable of capturing the most informative elements of the inputs [128].”
• Sequential Recommender Systems: Challenges, Progress and Prospects (2019, methodology): “They are widely incorporated into shallow networks [19] and RNN [25] to handle interaction sequences with noise.”