Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

• URL: http://arxiv.org/abs/2102.10550v1
• Date: Sun, 21 Feb 2021 08:29:41 GMT
• Title: Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network
• Authors: Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, Yong Li
• Abstract summary: We propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on heterogeneous information network (HIN) GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space.
• Score: 13.611161754155642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.

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