Memory Augmented Graph Neural Networks for Sequential Recommendation
arXiv:1912.11730
Abstract
The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.
Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020)
References in corpus (7)
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
- Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
- Translation-based Recommendation
- Self-Attentive Sequential Recommendation
- Signed Distance-based Deep Memory Recommender