Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-bassed recommender systems. Most of the existing deep learning-based ap-proaches utilize the recurrent neural network architecture or self-attention to model the sequential pat-terns and temporal influence among a user’s historical behavior and learn the user’s preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the se-quential recommendation. To address the above problems, in this article, we attempt to model us-er-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future.
Next, we introduce how to run our model for provided example data or your own data.
Python 3.7
Pytorch 1.7.1
Numpy 1.15.0
As an illustration, we provide the data and running command for MovieLens-1M.
ratings.dat：UserID::MovieID::Rating::Timestamp.
Liwei Huang, dr_huanglw@163.com
If you use PTGCN in your research, please cite our paper:
Liwei Huang, Yutao Ma, Yanbo Liu, Shuliang Wang, Deyi Li. 2021. Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations. http://arxiv.org/abs/2107.05235