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Hierarchical Gating Networks for Sequential Recommendation (1906.09217v1)

Published 21 Jun 2019 in cs.IR

Abstract: 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.

Citations (275)

Summary

  • The paper introduces a hierarchical gating network that integrates feature and instance gating to capture both short-term and long-term user interests.
  • It employs an item-item product module to explicitly model sequential relationships between items, enhancing prediction accuracy in sparse feedback scenarios.
  • Experiments on five real-world datasets demonstrate significant improvements in recall and NDCG over existing methods while achieving faster training speeds.

An Overview of Hierarchical Gating Networks for Sequential Recommendation

The paper Hierarchical Gating Networks for Sequential Recommendation provides an insightful approach to tackling the challenges faced by sequential recommender systems, particularly in the context of modeling both long-term and short-term user preferences in scenarios with sparse implicit feedback. The authors propose a hierarchical gating network (HGN) that is designed to capture these preferences effectively using a structured combination of feature gating, instance gating, and item-item relations.

Sequential recommendation systems aim to predict a user's next action based on their historical interaction sequence with items, emphasizing both the immediate past actions (short-term interests) and the broader user history (long-term interests). The primary contribution of this work lies in addressing the difficulty of accurately modeling these interests in the context of large-scale and sparse data typically found in recommender systems.

Key Innovations and Methodology

The framework introduced by the authors employs three core components:

  1. Feature Gating Module: This component is responsible for selecting relevant item features based on user-specific preferences. It ensures that only significant features that contribute to predicting future interactions are passed onto subsequent layers. The use of a gating mechanism inspired by gated linear units (GLUs) allows for adaptive selection of features, tailored by user embeddings.
  2. Instance Gating Module: Complementary to the feature gating, this module focuses on selecting important items from a user's recent interactions that are deemed significant for predicting upcoming items. This dual gating structure forms a hierarchical network that facilitates precise control over what information is propagated downstream.
  3. Item-Item Product Module: To address relationships between items directly, this module performs inner product operations between item embeddings to capture inherent item-item relations explicitly rather than implicitly, as often done in other models. This explicit modeling captures the sequential dynamics, thus highlighting patterns where specific item pairs are likely to be accessed one after the other.

Notably, the approach foregoes the use of traditional complex recurrent or convolutional neural network structures, opting instead for a lean architecture with fewer parameters and improved training efficiencies. Through the integration with Bayesian Personalized Ranking (BPR), the proposed method is further optimized for learning from implicit feedback, critical to delivering accurate top-N sequential recommendations.

Experimental Validation and Implications

The evaluation conducted across five real-world datasets showcases that HGN significantly outperforms contemporary state-of-the-art methods such as GRU4Rec, Caser, and SASRec, particularly in terms of recall and NDCG metrics. Specifically, the model achieves notable improvements in recall and enables faster training speeds, which is pertinent when dealing with extensive data sets typically seen in this domain.

The practical implications of this research are substantial for industries reliant on recommender systems, offering a method that balances the precision of user interest modeling with computational and operational efficiency. Theoretically, the model opens up avenues for further exploration into hierarchical mechanisms that potentially synergize short-term and long-term information processing in various machine learning applications.

Future Perspectives

Future research could involve extending the implications of hierarchical gating mechanisms to other types of sequential models beyond recommender systems, or investigating the potential of hybrid models that integrate further attention-based mechanisms alongside gating networks. Exploring scalability with extensive user interactions and adaptation to changing user behavior patterns over time remains a potential area for subsequent exploration. Additionally, understanding how user embeddings can be dynamically adjusted over time as more data becomes available could further improve the precision of sequential recommendations.