Demystifying graph neural networks in recommender systems

Ji, Houxiang

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https://hdl.handle.net/2142/113920 Copy

Description

Title

Demystifying graph neural networks in recommender systems

Author(s)

Ji, Houxiang

Issue Date

2021-12-06

Director of Research (if dissertation) or Advisor (if thesis)

Torrellas, Josep

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Graph neural network
• Recommender systems
• Microarchitecture

Abstract

Recommender systems have become indispensable tools for many applications with the explosive growth of online information. Recommender systems learn user's interest based on their profile and historical behavior and then recommend the item with the highest predicted ratings. Graph Neural Networks (GNNs) is a powerful graph representation learning method and have shown their unprecedented performance on many scenarios including natural language processing and computer vision. Most data required in the recommender systems is naturally and essentially represented with graph structure, for example, user-item interactions can be represented as a bipartite graph. With the superiority in graph learning, GNNs are gaining more and more attention in the field of recommender systems. This thesis presents our study of GNNs, which are employed in the recommender systems, to characterize their runtime behavior from various levels. To this end, we carefully profile GNN models on training and identify the most expensive operations which are worthy of attention for overhead reduction. We observe that the memory-intensive aggregation phase in the GNNs dominate the overall runtime different from the conventional deep neural network models. Further, we investigate the GNN's behavior at the micro-architectural level, which have received little attention so far, and summarize several key observations. Based on these insightful observations, we propose and discuss software and possible hardware mechanisms to optimize GNN-based recommender systems.

Graduation Semester

2021-12

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/113920

Copyright and License Information

Copyright 2021 Houxiang Ji

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