[연구] 박호건 교수 연구실(기계학습, 데이터마이닝 연구실)의 추천 시스템 연구The Web Conference (WWW) 2023 논문 게재 승인
- 소프트웨어융합대학
- 조회수1360
- 2023-02-23
박호건 교수 연구실(기계학습, 데이터마이닝 연구실)의 추천 시스템 연구The Web Conference (WWW) 2023 논문 게재 승인.
LearnData Lab(기계학습/데이터마이닝) 연구실(지도교수: 박호건, https://learndatalab.github.io)의정희수학생(석사과정)과박호건교수(교신저자)가제출한“Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based
Recommender Systems”논문이웹/데이터마이닝분야최우수학회 The Web Conference(WWW) 2023 (https://www2023.thewebconf.org) (BK IF=4)에게재승인되었고, 2023년 5월미국텍사스에서발표될예정입니다.
본논문은대부분의최신추천시스템의근간이되는그래프신경망(Graph neural networks; GNN)기반딥러닝모델에서사용가능한강화학습기반성능개선학습방법을제안합니다. 기존GNN 기반추천시스템은멀리떨어진이웃의정보를집계하여사용자와항목간의복잡한고차원적연결성을포착하는장점이있지만, 사용자와추천상품의이질적인특성으로인해성능향상에한계가있었습니다. 본논문에서는추천시스템을위한새로운강화학습기반메시지전달프레임워크인 DPAO(Dual Policy learning framework for Aggregation Optimization)를제안하며, 이중정책학습을사용하여사용자및상품에대한고차연결을적응적으로결정합니다. 제안한프레임워크는 Amazon, Yelp 포함 6개의실제상품추천데이터세트에서평가하였습니다. 그결과본논문이제안한프레임워크가최근발표된 GNN기반추천시스템모델을크게향상시켜, 대표적인추천시스템평가지표인nDCG와 Recall에서각각최대 63.7%와 42.9%까지향상시키는것으로나타났습니다. 구현코드와논문은본연구실홈페이지(https://learndatalab.github.io)에서확인할수있습니다.
[논문정보]
Heesoo Jung, Sangpil Kim, Hogun Park. Dual Policy Learning for Aggregation Optimization in Recommender Systems, In Proceedings of the ACM 32nd Web Conference: WWW 2023, Austin, USA, 2023.
[Abstract] Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users and items by aggregating information from distant neighbors and can improve the performance of recommender systems. Recently, Knowledge Graphs (KGs) have also been incorporated into the user-item interaction graph to provide more abundant contextual information; they are exploited to address cold-start problems and enable more explainable aggregation in GNN-based recommender systems (GNN-Rs). However, due to the heterogeneous nature of users and items, developing an effective aggregation strategy that works across multiple GNN-Rs, such as LightGCN and KGAT, remains a challenge. In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy learning framework for Aggregation Optimization). This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Dual policy learning leverages two Deep-Q-Network models to exploit the user- and item-aware feedback from a GNN-R and boost the performance of the target GNN-R. Our proposed framework was evaluated with both non-KG-based and KG-based GNN-R models on six real-world datasets, and their results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively.