Neural Attentive Personalization Model for Query Auto-Completion

Jiang, Danyang; Chen, Wanyu; Cai, Fei; Chen, Honghui

doi:10.1109/iaeac.2018.8577694

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…One advantage of the neural language modeling architecture is that additional features can be seamlessly incorporated. For example, personalization can be modeled by incorporating user ID embeddings [11,16,17] in the network. Time aware [11] and spelling errors aware [40] models are also developed under this framework.…”

Section: Deep Learning Approach For Qacmentioning

confidence: 99%

Efficient Neural Query Auto Completion

Wang

Guo

Gao

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates according to query prefixes, and ranking them based on extracted features. Three major challenges are observed for a query auto completion system: (1) QAC has a strict online latency requirement. For each keystroke, results must be returned within tens of milliseconds, which poses a significant challenge in designing sophisticated language models for it. (2) For unseen queries, generated candidates are of poor quality as contextual information is not fully utilized. (3) Traditional QAC systems heavily rely on handcrafted features such as the query candidate frequency in search logs, lacking sufficient semantic understanding of the candidate.In this paper, we propose an efficient neural QAC system with effective context modeling to overcome these challenges. On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin. On the candidate ranking side, an unnormalized language model is proposed, which effectively captures deep semantics of queries. This approach presents better ranking performance over state-of-the-art neural ranking methods and reduces ∼95% latency compared to neural language modeling methods. The empirical results on public datasets show that our model achieves a good balance between accuracy and efficiency. This system is served in LinkedIn job search with significant product impact observed.

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Section: Deep Learning Approach For Qacmentioning

confidence: 99%

Efficient Neural Query Auto Completion

Wang

Guo

Gao

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…Recurrent Neural Networks (RNN) [11] have also been studied for QAC. ree RNN models -session-based, personalized, and a ention based, have been proposed in [12]. Fiorini and Lu [9] use user history based features as well as time features as input to an RNN model.…”

Section: Related Workmentioning

confidence: 99%

Personalized Query Auto-Completion Through a Lightweight Representation of the User Context

Kannadasan¹,

Aslanyan²

2019

Preprint

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ery Auto-Completion (QAC) is a widely used feature in many domains, including web and eCommerce search. is feature suggests full queries based on a pre x of a few characters typed by the user. QAC has been extensively studied in the literature in the recent years, and it has been consistently shown that adding personalization features can signi cantly improve the performance of the QAC model. In this work we propose a novel method for personalized QAC that uses lightweight embeddings learnt through fastText [2, 14]. We construct an embedding for the user context queries, which are the last few queries issued by the user. We also use the same model to get the embedding for the candidate queries to be ranked. We introduce ranking features that compute the distance between the candidate queries and the context queries in the embedding space. ese features are then combined with other commonly used QAC ranking features to learn a ranking model using the state of the art LambdaMART ranker [3]. We apply our method to a large eCommerce search engine (eBay) and show that the ranker with our proposed feature signi cantly outperforms the baselines on all of the o ine metrics measured, which includes Mean Reciprocal Rank (MRR), Success Rate (SR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Our baselines include the Most Popular Completion (MPC) model which is a commonly used baseline in the QAC literature, as well as a ranking model without our proposed features.e ranking model with the proposed features results in a 20 − 30% improvement over the MPC model on all metrics. We obtain up to a 5% improvement over the baseline ranking model for all the sessions, which goes up to about 10% when we restrict to sessions that contain the user context. Moreover, our proposed features also signi cantly outperform text based personalization features studied in the literature before, and adding text based features on top of our proposed embedding based features results only in minor improvements.

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“…To alleviate the shortcomings of the retrieve-and-rank framework, another line of research uses generative sequence models to generate potential query completions, starting from the given prefix and conditioned on previous queries [10,17,35,38]. Such models leverage powerful deep learning models that can generate novel queries as well as generalize to unseen prefixes.…”

Section: Introductionmentioning

confidence: 99%

Session-Aware Query Auto-completion using Extreme Multi-Label Ranking

Yadav

Sen

Hill

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar. Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix. Such session-aware QACs can be generated by recent sequence-to-sequence deep learning models; however, these generative approaches often do not meet the stringent latency requirements of responding to each user keystroke. Moreover, these generative approaches pose the risk of showing nonsensical queries. One can pre-compute a relatively small subset of relevant queries for common prefixes and rank them based on the context. However, such an approach fails when no relevant queries for the current context are present in the pre-computed set.

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Neural Attentive Personalization Model for Query Auto-Completion

Cited by 9 publications

References 16 publications

Efficient Neural Query Auto Completion

Efficient Neural Query Auto Completion

Personalized Query Auto-Completion Through a Lightweight Representation of the User Context

Session-Aware Query Auto-completion using Extreme Multi-Label Ranking

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