Efficient interactive fuzzy keyword search

Ji, Shengyue; Li, Guoliang; Li, Chen; Feng, Jianhua

doi:10.1145/1526709.1526760

Cited by 155 publications

(124 citation statements)

References 16 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…The comparator ranks the segmentation that has the smaller minimum edit distance summation higher. If two segmentations have the same total minimum edit distance, then it ranks the segmentation with fewer segments higher [10], [11], [12]. …”

Section: Top-k Querymentioning

confidence: 99%

Efficient Proximity Search with Query Logs

Holambe¹,

Patil²

2015

IJCA

View full text Add to dashboard Cite

In information retrieval technology there are various techniques for fetching data from resources. And that technique also contains various issues. Information retrieval techniques require advanced manipulating schemes which improves keyword search. There are many techniques have been proposed but results get down when large amount data interrupted. In this paper, have tendency to achieve efficient time and space complexities by integrating proximity information. This system improves the performance by using previous searching results. All the previous system consist basic solutions for extracting results and ranking them. Query logs consists the last searching results and use that results for next search. Fuzzy keyword search truly enhance the system usability. Existing system in databases requires to write complete keyword for searching but by using auto-complete scheme it is easy to type less and find more. In this system proper demand paging algorithm is used for finding previous results. General TermsAlgorithm, Performance.

show abstract

Section: Top-k Querymentioning

confidence: 99%

Efficient Proximity Search with Query Logs

Holambe¹,

Patil²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…For example, [4,10] studied the space efficiency of index for QAC. [21,11] investigated the efficient algorithms for QAC. [7] addressed the problem of suggesting query completions even if the prefix is mis-spelled.…”

Section: Related Workmentioning

confidence: 99%

“…With the prevalence of mobile devices, it becomes more critical because typing takes more effort in mobile devices than in PCs. Previous studies addressed the QAC problem in different perspectives, ranging from designing more efficient indexes and algorithms [4,10,21,11], leveraging context in long and short term query history [3], learning to combine more personalized signals such as gender, age and location [19], suggesting queries from a mis-spelled prefix [7].…”

Section: Introductionmentioning

confidence: 99%

A two-dimensional click model for query auto-completion

Dong

Wang

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

View full text Add to dashboard Cite

Query auto-completion (QAC) facilitates faster user query input by predicting users' intended queries. Most QAC algorithms take a learning-based approach to incorporate various signals for query relevance prediction. However, such models are trained on simulated user inputs from query log data. The lack of real user interaction data in the QAC process prevents them from further improving the QAC performance.In this work, for the first time we collect a high-resolution QAC query log that records every keystroke in a QAC session. Based on this data, we discover two user behaviors, namely the horizontal skipping bias and vertical position bias which are crucial for relevance prediction in QAC. In order to better explain them, we propose a novel two-dimensional click model for modeling the QAC process with emphasis on these behaviors.Extensive experiments on our QAC data set from both PC and mobile devices demonstrate that our proposed model can accurately explain the users' behaviors in interacting with a QAC system, and the resulting relevance model significant improves the QAC performance over existing click models. Furthermore, the learned knowledge about the skipping behavior can be effectively incorporated into existing learning-based models to further improve their performance.

show abstract

“…Support Vector machine is used in [6] to rank the results and accessing the large amount is tedious task and so new information access paradigms is introduced in [7] to improve the search quality. Leah S. Larkey proposes a system in [8] for searching and classifying the patent and it fully based on Inquery.…”

Section: Related Workmentioning

confidence: 99%