NED: Niche Detection in User Content Consumption Data
NED: Niche Detection in User Content Consumption Data
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DOI：
10.1145/3459637.3482455
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发表时间：
2021-10
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影响因子：
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通讯作者：
Ekta Gujral;Leonardo Neves;E. Papalexakis;Neil Shah
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作者：
Ekta Gujral;Leonardo Neves;E. Papalexakis;Neil Shah
近年来，可解释的机器学习方法引起了越来越多的兴趣。在这项工作中，我们提出并研究了生态位检测问题，该问题对跨两种模式的共聚类相互作用的经典问题施加了一个可解释的透镜。在生态位检测问题中，我们的目标是识别具有节点属性导向解释的生态位或共簇。利基检测适用于许多社交内容消费场景，其最终目标是描述和提取关于用户-内容关联的高级见解：不仅某些用户喜欢某些类型的内容，而且通过节点属性解释用户和内容的类型。例如具有“谁买什么”交互以及用户和产品属性的电子商务平台，或者具有“谁叫谁”交互以及用户属性的移动呼叫平台。发现和描述利基对理解用户行为、营销和有针对性的内容生产具有重要意义。与之前的工作不同，我们的重点是可解释方法和共聚类的交集。首先，我们形式化了生态位检测问题并进行了初步讨论。接下来，我们设计了一个端到端框架NED，它分为两个步骤：基于交互密度发现用户行为的共聚类，并使用相关节点的属性解释它们。最后，我们展示了几个公共数据集的实验结果，以及来自Snapchat的大规模工业数据集，表明与最先进的方法相比，NED在共聚类（20%的准确率）和解释相关目标（12%的平均精度）方面都有所提高。
Explainable machine learning methods have attracted increased interest in recent years. In this work, we pose and study the niche detection problem, which imposes an explainable lens on the classical problem of co-clustering interactions across two modes. In the niche detection problem, our goal is to identify niches, or co-clusters with node-attribute oriented explanations. Niche detection is applicable to many social content consumption scenarios, where an end goal is to describe and distill high-level insights about user-content associations: not only that certain users like certain types of content, but rather the types of users and content, explained via node attributes. Some examples are an e-commerce platform with who-buys-what interactions and user and product attributes, or a mobile call platform with who-calls-whom interactions and user attributes. Discovering and characterizing niches has powerful implications for user behavior understanding, as well as marketing and targeted content production. Unlike prior works, ours focuses on the intersection of explainable methods and co-clustering. First, we formalize the niche detection problem and discuss preliminaries. Next, we design an end-to-end framework, NED, which operates in two steps: discovering co-clusters of user behaviors based on interaction densities, and explaining them using attributes of involved nodes. Finally, we show experimental results on several public datasets, as well as a large-scale industrial dataset from Snapchat, demonstrating that NED improves in both co-clustering (20% accuracy) and explanation-related objectives (12% average precision) compared to state-of-the-art methods.