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Graph Convolutional Embeddings for Recommender Systems
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DOI：10.1109/ACCESS.2021.3096609.png)
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摘要
En 中文
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the raw matrix that represents interactions. However, we can increase the performance of RS by considering other kinds of signals like the context of interactions, which could be, for example, the time or date of the interaction, the user location, or sequential data corresponding to the historical interactions of the user with the system. These complex, context-based interaction signals are characterized by a rich relational structure that can be represented by a multi-partite graph. Graph Convolutional Networks (GCNs) have been used successfully in collaborative filtering with simple user-item interaction data. In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures. More specifically, we define a graph convolutional embedding layer for N-partite graphs that processes user-item-context interactions and constructs node embeddings by leveraging their relational structure. Experiments on several datasets show the benefits of the introduced GCN embedding layer by measuring the performance of different context-enriched tasks.
Keyword:
Convolution
Frequency modulation
Context modeling
Collaboration
Tensors
Data models
Task analysis
Graph convolutional network
interaction context
embedding layer
factorization machines
neural networks
collaborative filtering
context-aware recommendation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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