{"type":"ScholarlyArticle","title":"Graph Collaborative Expert Finding with Contrastive Learning","authors":[{"name":"Qiyao Peng","slug":"qiyao-peng","given":"Qiyao","family":"Peng","affiliation":"School of New Media and Communication , Tianjin University , Tianjin , China"},{"name":"Wenjun Wang","slug":"wenjun-wang","given":"Wenjun","family":"Wang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China; Georgia Tech Shenzhen Institute , Tianjin University , Guangdong , China; Yazhou Bay Innovation Institute , Hainan Tropical Ocean University , Sanya Hainan , China"},{"name":"Hongtao Liu","slug":"hongtao-liu","given":"Hongtao","family":"Liu","affiliation":"Du Xiaoman Finicial Technology , Beijing , China"},{"name":"Cuiying Huo","slug":"cuiying-huo","given":"Cuiying","family":"Huo","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Minglai Shao","slug":"minglai-shao","given":"Minglai","family":"Shao","affiliation":"School of New Media and Communication , Tianjin University , Tianjin , China"}],"abstract":"In Community Question Answering (CQA) websites, most current expert finding methods often model expert embeddings from textual features and optimize them with expert-question first-order interactions, i.e., this expert has answered this question. In this paper, we try to address the limitation of current models that typically neglect the intrinsic high-order connectivity within expert-question interactions, which is pivotal for collaborative effects. We introduce an innovative and simple approach: by conceptualizing expert-question interactions as a bipartite graph, and then we propose a novel graph-based expert finding method based on contrastive learning to effectively capture both first-order and intricate high-order connectivity, named CGEF. Specifically, we employ a question encoder to model questions from titles and employ the graph attention network to recursively propagate embeddings. Besides, to alleviate the problem of sparse interactions, we devise two auxiliary tasks to enhance expert modeling. First, we generate multiple views of one expert, including: 1) behavior-level augmentation drops interaction edges randomly in the graph; 2) interest-level augmentation randomly replaces question titles with tags in the graph. Then we maximize the agreement between one expert and the corresponding augmented expert on a specific view. In this way, the model can effectively inject collaborative signals into expert modeling. Extensive experiments on six CQA datasets demonstrate significant improvements compared with recent methods.","doi":"10.24963/ijcai.2024/253","url":"https://ijcai.org/proceedings/2024/253","pdf":"https://ijcai.org/proceedings/2024/0253.pdf","markdown":"https://ijcai.org/proceedings/2024/253.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/253","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":253,"pages":{"first":2288,"last":2296},"keywords":["Data Mining: DM: Networks","Data Mining: DM: Recommender systems","Data Mining: DM: Mining text, web, social media","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Amendola et al., 2024] Maddalena Amendola, Andrea Pas- sarella, and Raffaele Perego. 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