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首页> 外文期刊>International journal of information retrieval research >Intent-Based User Segmentation with Query Enhancement
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Intent-Based User Segmentation with Query Enhancement

机译：具有查询增强功能的基于意图的用户细分

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摘要

With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors 'methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user s search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.
机译：随着互联网的飞速发展，对用户搜索查询基础的在线意图的准确预测越来越受到在线广告界的关注。本文旨在通过提出一种查询增强机制来利用行为查询广告来解决用户查询的主要挑战，该机制通过利用用户查询日志来增强用户的查询。实证评估表明，在基于意图的用户分类和用户细分方面，作者的查询增强方法比基线模型有更大的改进。与传统的用户细分方法不同，传统的用户细分方法很少考虑用户行为的语义，作者提出了一种新颖的用户细分策略，该方法通过将查询增强机制与主题模型相结合来挖掘用户与其行为之间的关系，从而对用户进行细分。语义方式。与经典聚类算法K-means相比，实验结果表明，所提出的用户细分策略有助于显着提高行为靶向效果。本文还提出了一种替代方法，以在评估数据集的情况下为评估目的定义用户的搜索意图。这种方法会自动在点击图中标记用户，然后将其用于训练基于意图的用户分类器。

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