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Use of Microblog Behavior Data in a Language Modeling Framework to Enhance Web Search Personalization

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Information Retrieval Technology (AIRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9994))

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Abstract

Diversity in users’ information needs has been effectively dealt with through personalized Web search systems whereby a user’s interests and preferences are taken into account within the retrieval model. A significant component of any Web search personalization model is the means with which to model a user’s interests and preferences to build what is termed as a user profile. This work explores the use of the Twitter microblog network as a source of user profile construction for Web search personalization. We propose a statistical language modeling approach taking into account various aspects of a user’s behavior on the Twitter network (such as Twitterers followed, mentioned and retweeted). The model also incorporates network and topical similarity measures which enables the model to be a better representation of the user’s profile. The richness of the Web search personalization model leads to significant performance improvements in retrieval accuracy.

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Notes

  1. 1.

    A query such as “Python” may refer to the programming language or the snake (Example from [17]).

  2. 2.

    http://twitter.com.

  3. 3.

    Earlier work used a network similarity threshold based on which Twitterers not similar to the target user were excluded from the model [26].

  4. 4.

    Note that previous work compared our approach against a non-personalized baseline.

  5. 5.

    From this point onwards in the paper we use the phrase “target user” to refer to the user performing the search and for whom we want to personalize search results.

  6. 6.

    This is more suited to the task at hand as tweets are short and in general related to a single topic.

  7. 7.

    It is often the case that random acquaintances are also followed on Twitter.

  8. 8.

    Note that we treat the tweets’ data as equivalent to history and user documents’ data; furthermore, the technique by Matthijs and Radlinski utilized various segments of a web page (such as title, web page metadata which we could not utilize and hence, we use all terms in tweets except for stopwords).

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Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Arjumand Younus

  2. Computational Intelligence Research Group, Information Technology, National University of Ireland, Galway, Ireland

    Arjumand Younus

Authors
  1. Arjumand Younus
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Correspondence to Arjumand Younus .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Shaoping Ma

  2. Renmin University of China , Beijing, China

    Ji-Rong Wen

  3. Tsinghua University , Beijing, China

    Yiqun Liu

  4. Renmin University of China , Beijing, China

    Zhicheng Dou

  5. Tsinghua University , Beijing, China

    Min Zhang

  6. Yahoo Labs , Sunnyvale, California, USA

    Yi Chang

  7. Renmin University of China , Beijing, China

    Xin Zhao

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Younus, A. (2016). Use of Microblog Behavior Data in a Language Modeling Framework to Enhance Web Search Personalization. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-48051-0_13

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  • Online ISBN: 978-3-319-48051-0

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