Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
This work addresses the challenge of improving recommendation accuracy for users in streaming platforms by leveraging item attributes, though it appears incremental as it builds on existing GNN and attention methods.
The paper tackles the problem of sequential recommendation by incorporating item attribute information, which is often ignored, and proposes Murzim, an attribute-augmented graph neural network model. It shows that Murzim outperforms state-of-the-art methods in recall and MRR on multiple datasets and has been deployed on a large streaming platform.
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-augmented graph neural network model named Murzim. Murzim takes as input the graphs constructed from the user-item interaction sequences and corresponding item attribute sequences. By combining the GNNs with node aggregation and an attention network, Murzim can capture user preference patterns, generate embeddings for user-item interaction sequences, and then generate recommendations through next-item prediction. We conduct extensive experiments on multiple datasets. Experimental results show that Murzim outperforms several state-of-the-art methods in terms of recall and MRR, which illustrates that Murzim can make use of item attribute information to produce better recommendations. At present, Murzim has been deployed in MX Player, one of India's largest streaming platforms, and is recommending videos for tens of thousands of users.