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trie-nlg: trie context augmentation to improve personalized query auto-completion for short and unseen prefixes

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Abstract

Query auto-completion (QAC) aims at suggesting plausible completions for a given query prefix. Traditionally, QAC systems have leveraged tries curated from historical query logs to suggest most popular completions. In this context, there are two specific scenarios that are difficult to handle for any QAC system: short prefixes (which are inherently ambiguous) and unseen prefixes. Recently, personalized Natural Language Generation (NLG) models have been proposed to leverage previous session queries as context for addressing these two challenges. However, such NLG models suffer from two drawbacks: (1) some of the previous session queries could be noisy and irrelevant to the user intent for the current prefix, and (2) NLG models cannot directly incorporate historical query popularity. This motivates us to propose a novel NLG model for QAC, Trie-NLG, which jointly leverages popularity signals from trie and personalization signals from previous session queries. We train the Trie-NLG model by augmenting the prefix with rich context comprising of recent session queries and top trie completions. This simple modeling approach overcomes the limitations of trie-based and NLG-based approaches, and leads to state-of-the-art performance. We evaluate the Trie-NLG model using two large QAC datasets. On average, our model achieves huge \(\sim\)57% and \(\sim\)14% boost in MRR over the popular trie-based lookup and the strong BART-based baseline methods, respectively. We make our code publicly available at https://github.com/kaushal0494/Trie-NLG.

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Availability of data and materials

We make our code available as supplementary information. It is in line with our claims in the paper and can be trusted to reproduce the results. We use one public dataset (AOL) and a Bing dataset. Since Bing data is proprietary, it cannot be made publicly available.

Code availability

We make our code publicly available and it is attached as supplementary data.

Notes

  1. A small percent of suggestions are not prefix preserving; in this work, we focus on prefix-preserving suggestions only.

  2. https://github.com/jma127/pyltr.

  3. https://github.com/asyml/texar.

  4. https://huggingface.co/.

  5. https://huggingface.co/blog/how-to-generate#appendix.

  6. https://github.com/kaushal0494/Trie-NLG.

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Funding

This research was partially funded by Microsoft Academic Partnership Grant 2022.

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Authors and Affiliations

  1. IIT Hyderabad, Hyderabad, Telangana, 502284, India

    Kaushal Kumar Maurya & Maunendra Sankar Desarkar

  2. Microsoft India, Hyderabad, Telangana, 500032, India

    Manish Gupta & Puneet Agrawal

Authors
  1. Kaushal Kumar Maurya
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  2. Maunendra Sankar Desarkar
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  3. Manish Gupta
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  4. Puneet Agrawal
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Contributions

All authors contributed to the design of the project. Specifically, Conceptualization, Methodology, Data preparation: [KKM]; Writing—original draft preparation: [KKM, MG, MSD]; Writing—review and editing: [everyone]; Funding acquisition, Resources, Supervision: [MG, MSD, PA].

Corresponding author

Correspondence to Kaushal Kumar Maurya.

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Responsible editor: Charalampos Tsourakakis.

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Maurya, K.K., Desarkar, M.S., Gupta, M. et al. trie-nlg: trie context augmentation to improve personalized query auto-completion for short and unseen prefixes. Data Min Knowl Disc 37, 2306–2329 (2023). https://doi.org/10.1007/s10618-023-00966-0

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