Memory Augmented Graph Neural Networks for Sequential Recommendation

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.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

Sequential Recommendation with Link-Prediction on Graphs Meta-Learning

Graduate School of Artificial IntelligenceIn this paper, we propose a novel framework called Sequential Graph Meta-learning (SGM) to address the problem of sequential recommendation, which involves predicting the next item based on a user???s historical behavior. SGM introduces a graph-based representation that captures the relationships between users and items, leveraging them as nodes and their interactions as edges. By extracting meaningful node embeddings, our model effectively encodes the complex relationships within the graph. Furthermore, we utilize subgraphs that represent user-item interactions with meta-learning, enabling the model to adapt and reflect as time change. Specifically, our approach focuses on link prediction, aiming to predict whether a user will interact with a specific item in the future. Through extensive experiments, we demonstrate that our SGM framework outperforms previous models in most scenarios by significant margins. This highlights the effectiveness of our proposed approach in addressing the challenges of sequential recommendation and enhancing recommendation accuracy.clos