Hierarchical Gating Networks for Sequential Recommendation

21 Jun 2019  ยท  Chen Ma, Peng Kang, Xue Liu ยท

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.

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Datasets


Results from the Paper


 Ranked #1 on Recommendation Systems on Amazon-CDs (Recall@10 metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Recommendation Systems Amazon-CDs HGN Recall@10 0.0426 # 1
nDCG@10 0.0233 # 1
Recommendation Systems GoodReads-Children HGN Recall@10 0.1263 # 1
nDCG@10 0.113 # 1
Recommendation Systems GoodReads-Comics HGN Recall@10 0.1743 # 1
nDCG@10 0.1927 # 1
Recommendation Systems MovieLens 20M HGN nDCG@10 0.1195 # 4
Recall@10 0.1255 # 2

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