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Intent-Based User Segmentation with Query Enhancement

Intent-Based User Segmentation with Query Enhancement

Wei Xiong, Michael Recce, Brook Wu
Copyright: © 2013 |Volume: 3 |Issue: 4 |Pages: 17
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781466634893|DOI: 10.4018/ijirr.2013100101
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MLA

Xiong, Wei, et al. "Intent-Based User Segmentation with Query Enhancement." IJIRR vol.3, no.4 2013: pp.1-17. http://doi.org/10.4018/ijirr.2013100101

APA

Xiong, W., Recce, M., & Wu, B. (2013). Intent-Based User Segmentation with Query Enhancement. International Journal of Information Retrieval Research (IJIRR), 3(4), 1-17. http://doi.org/10.4018/ijirr.2013100101

Chicago

Xiong, Wei, Michael Recce, and Brook Wu. "Intent-Based User Segmentation with Query Enhancement," International Journal of Information Retrieval Research (IJIRR) 3, no.4: 1-17. http://doi.org/10.4018/ijirr.2013100101

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Abstract

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.

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