Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes Article Swipe
Kaushal Kumar Maurya
,
Maunendra Sankar Desarkar
,
Manish Gupta
,
Puneet Agrawal
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.15455
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.15455
Query auto-completion (QAC) aims to suggest 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 ~57% and ~14% boost in MRR over the popular trie-based lookup and the strong BART-based baseline methods, respectively. We make our code publicly available.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.15455
- https://arxiv.org/pdf/2307.15455
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385436693
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4385436693Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.15455Digital Object Identifier
- Title
-
Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen PrefixesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-28Full publication date if available
- Authors
-
Kaushal Kumar Maurya, Maunendra Sankar Desarkar, Manish Gupta, Puneet AgrawalList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.15455Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2307.15455Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.15455Direct OA link when available
- Concepts
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Trie, Computer science, Prefix, Context (archaeology), Session (web analytics), Information retrieval, Theoretical computer science, Data structure, Programming language, World Wide Web, Linguistics, Paleontology, Biology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.plausible | 6 |
| abstract_inverted_index.prefixes. | 54 |
| abstract_inverted_index.scenarios | 36 |
| abstract_inverted_index.BART-based | 209 |
| abstract_inverted_index.Generation | 59 |
| abstract_inverted_index.addressing | 73 |
| abstract_inverted_index.ambiguous) | 51 |
| abstract_inverted_index.approaches | 173 |
| abstract_inverted_index.augmenting | 147 |
| abstract_inverted_index.available. | 218 |
| abstract_inverted_index.comprising | 153 |
| abstract_inverted_index.drawbacks: | 84 |
| abstract_inverted_index.historical | 21, 112 |
| abstract_inverted_index.inherently | 50 |
| abstract_inverted_index.irrelevant | 96 |
| abstract_inverted_index.popularity | 130 |
| abstract_inverted_index.trie-based | 170, 204 |
| abstract_inverted_index.challenges. | 76 |
| abstract_inverted_index.completions | 7 |
| abstract_inverted_index.incorporate | 111 |
| abstract_inverted_index.limitations | 168 |
| abstract_inverted_index.popularity. | 114 |
| abstract_inverted_index.completions. | 28, 161 |
| abstract_inverted_index.performance. | 178 |
| abstract_inverted_index.personalized | 56 |
| abstract_inverted_index.respectively. | 212 |
| abstract_inverted_index.Traditionally, | 13 |
| abstract_inverted_index.auto-completion | 1 |
| abstract_inverted_index.personalization | 135 |
| abstract_inverted_index.state-of-the-art | 177 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.10063438 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |