LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation
LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation
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DOI：
10.1145/3469887
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发表时间：
2021-09
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影响因子：
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通讯作者：
Jun Yang;Weizhi Ma;Min Zhang;Xin Zhou;Yiqun Liu;Shaoping Ma
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作者：
Jun Yang;Weizhi Ma;Min Zhang;Xin Zhou;Yiqun Liu;Shaoping Ma
法律场景推荐（legal - rec）是一项专门的推荐任务，旨在为用户提供潜在的有用的法律文件。而与传统推荐相比，主要有三点不同：(1)在legal - rec场景中，法律信息的结构联系和文本内容都很重要，这意味着特征融合在这里非常重要。(2) legal - rec用户更喜欢最新的法律案例（最新的法律解释和法律实践），这导致了严重的新条目问题。(3)与其他场景的用户不同，Legal-Rec的用户多为专家用户和领域相关用户。他们往往集中于几个主题，有更稳定的信息需求。因此，在这里准确地模拟用户兴趣是很重要的。据我们所知，现有的推荐工作无法同时处理这些挑战。为了解决这些挑战，我们提出了一个基于法律信息增强的图神经网络推荐框架（LegalGNN）。首先，设计统一的法律内容和结构表示模型进行特征融合，构建异构法律信息网络（HLIN），将结构特征（如知识图谱）和语境特征（如法律文件内容）连接起来进行训练。其次，为了模拟用户兴趣，我们将法律系统中用户发出的查询合并到HLIN中，并将它们与检索到的文档和被查询的用户链接起来。这些额外的信息不仅有助于估计用户偏好，而且对这种情况下的冷用户/项目（交互历史较少）也很有价值。第三，利用具有关联注意机制的图神经网络，利用HLIN中的高阶连接。在现实世界法律数据集上的实验结果验证了LegalGNN显着优于几种最先进的方法。据我们所知，LegalGNN是第一个用于法律推荐的图神经模型。
Recommendation in legal scenario (Legal-Rec) is a specialized recommendation task that aims to provide potential helpful legal documents for users. While there are mainly three differences compared with traditional recommendation: (1) Both the structural connections and textual contents of legal information are important in the Legal-Rec scenario, which means feature fusion is very important here. (2) Legal-Rec users prefer the newest legal cases (the latest legal interpretation and legal practice), which leads to a severe new-item problem. (3) Different from users in other scenarios, most Legal-Rec users are expert and domain-related users. They often concentrate on several topics and have more stable information needs. So it is important to accurately model user interests here. To the best of our knowledge, existing recommendation work cannot handle these challenges simultaneously. To address these challenges, we propose a legal information enhanced graph neural network–based recommendation framework (LegalGNN). First, a unified legal content and structure representation model is designed for feature fusion, where the Heterogeneous Legal Information Network (HLIN) is constructed to connect the structural features (e.g., knowledge graph) and contextual features (e.g., the content of legal documents) for training. Second, to model user interests, we incorporate the queries users issued in legal systems into the HLIN and link them with both retrieved documents and inquired users. This extra information is not only helpful for estimating user preferences, but also valuable for cold users/items (with less interaction history) in this scenario. Third, a graph neural network with relational attention mechanism is applied to make use of high-order connections in HLIN for Legal-Rec. Experimental results on a real-world legal dataset verify that LegalGNN outperforms several state-of-the-art methods significantly. As far as we know, LegalGNN is the first graph neural model for legal recommendation.