Exploring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion

Li, Liangda; Deng, Hongbo; Dong, Anlei; Chang, Yi; Baeza-Yates, Ricardo; Zha, Hongyuan

doi:10.1145/3038912.3052593

Cited by 25 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After candidates are generated, candidate ranking components compute a relevance score for each candidate. Different sources of information have been exploited to improve the ranking, including context or session information [1,18], time/popularity-sensitivity [6,36,41], personalization [6,35], user behaviors [15,22,28,43], and click through logs [21]. Our paper does not use any of these additional information and focuses on the effectiveness and efficiency of QAC given general query logs.…”

Section: Traditional Approaches 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

View full text Add to dashboard Cite

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.

show abstract

Section: Traditional Approaches 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

View full text Add to dashboard Cite

show abstract

“…User query log-based query suggestions are one of the main subfields of query suggestions [4], [10]- [12]. User log-based query suggestions learn the user's query log, and then generate relevant queries as suggestions.…”

Section: A User Query Log Based Query Suggestionmentioning

confidence: 99%

Developing a Meta-Suggestion Engine for Search Queries

Kim

2022

IEEE Access

View full text Add to dashboard Cite

Typically, search engines provide query suggestions to assist users in the search process. Query suggestions are very important for improving user's search experience. However, most query suggestions are based on the user's search logs, and they can be influenced by infrequently searched queries. Depending on the user's query, query suggestions can be ineffective in global search engines but effective in a domestic search engine. Conversely, it can be effective in global engines and weak in domestic engines. In addition, log-based query suggestions require many search logs, which makes them difficult to construct outside of a large search engine. Some search engines do not provide query suggestions, making searches difficult for users. These query suggestion vulnerabilities degrade the user's search experience. In this study, we develop a meta-suggestion, a new query suggestion scheme. Similar to meta-searches, meta-suggestions retrieve candidate queries of suggestions from other search engines. Meta-suggestions generate suggestions by reranking the aggregated candidate queries. We develop a meta-suggestion engine (MSE) browser extension that generates meta-suggestions. It can provide query suggestions for any webpage and does not require a search log. Comparing our meta-suggestions to major search engines such as Google, showed a 17% performance improvement on normalized discounted cumulative gain (NDCG) and a 31% improvement on precision. If more detailed factors, such as user preferences are discovered through continued research, it is expected that user searches will greatly improve. An enhanced user search experience is possible if factors, such as user preference, are examined in future work.

show abstract

“…Spelling correction is also widely applied in web query search, completion and suggestion (Chen et al, 2007), where interaction with users is embedded in the model such as noisy channels (Duan and Hsu, 2011) and graphical models (Li et al, 2017). However, click log or other external resource is required to assist the correction, which is not feasible in the low-resource setting.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages

Anastasopoulos

Black

2020

Preprint

View full text Add to dashboard Cite

Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict and large corpora are usually required to collect enough examples. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected, for example within a chat app. Such models are designed to be incrementally improved as feedback is given from users. In this work, we design a knowledge-base and prediction model embedded system for spelling correction in low-resource languages. Experimental results on multiple languages show that the model could become effective with a small amount of data. We perform experiments on both natural and synthetic data, as well as on data from two endangered languages (Ainu and Griko). Last, we built a prototype system that was used for a small case study on Hinglish, which further demonstrated the suitability of our approach in real world scenarios.

show abstract

Exploring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion

Cited by 25 publications

References 30 publications

Efficient Neural Query Auto Completion

Efficient Neural Query Auto Completion

Developing a Meta-Suggestion Engine for Search Queries

Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages

Contact Info

Product

Resources

About