Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge. Traditional recommendation systems (RS) mostly enrich user-item interactions where the context consists of static user profiles and item descriptions, ignoring the contextual logic and constraints that underlie them. For example, an RS may recommend an item on the condition that the user has interacted with another item as its prerequisite. Modeling prerequisite context from conceptual side information can overcome this weakness. We propose Prerequisite Driven Recommendation (PDR), a generic context-aware framework where prerequisite context is explicitly modeled to facilitate recommendation. We first design a Prerequisite Knowledge Linking (PKL) algorithm, to curate datasets facilitating PDR research. Employing it, we build a 75k+ high-quality prerequisite concept dataset which spans three domains. We then contribute PDRS, a neural instantiation of PDR. By jointly optimizing both the prerequisite learning and recommendation tasks through multi-layer perceptrons, we find PDRS consistently outperforms baseline models in all three domains, by an average margin of 7.41%. Importantly, PDRS performs especially well in cold-start scenarios with improvements of up to 17.65%.
翻译:传统建议系统(RS)主要丰富用户-项目互动,其背景由静态用户概况和项目描述组成,忽视其背景逻辑和制约。例如,一个RS可以建议一个项目,条件是用户已经与另一个项目互动作为其先决条件。从概念侧信息建模的先决条件可以克服这一弱点。我们提出预先设定驱动器建议(PDR),这是一个通用背景认知框架,其前提框架是明确为便利建议而建模。我们首先设计了一个先设知识链接(PKL)算法,以整理数据集,便利PDR的研究。使用它,我们建立一个75k+高质量先决条件概念数据集,该数据集跨越三个领域。我们然后提供DPRS,一个PDR的神经瞬间回化。我们通过多级透视器共同优化先决条件学习和建议任务,我们发现PDRS始终超越所有三个领域的基线模型,以7.41%至17.65的平均值为起点。DRSS将特别地进行冷至17.65的改进。