QRM: A Probabilistic Model for Search Engine Query Recommendation

2014 ◽  
pp. 665-676 ◽  
Author(s):  
JianGuo Wang ◽  
Joshua Zhexue Huang
Keyword(s):  
Search Engine ◽  
Probabilistic Model ◽  
Query Recommendation

scholarly journals Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engine

2017 ◽  
Vol 67 ◽  
pp. 1-10 ◽  
Author(s):  
David A. Hanauer ◽  
Danny T.Y. Wu ◽  
Lei Yang ◽  
Qiaozhu Mei ◽  
Katherine B. Murkowski-Steffy ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Search Engine ◽  
Health Record ◽  
Query Recommendation ◽  
Electronic Health ◽  
User Centered

scholarly journals Recommending High Utility Queries via Query-Reformulation Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
JianGuo Wang ◽  
Joshua Zhexue Huang ◽  
Dingming Wu
Keyword(s):  
Random Walk ◽  
Search Engine ◽  
State Of The Art ◽  
Transition Probability ◽  
Experimental Results ◽  
Query Reformulation ◽  
Search Results ◽  
Query Recommendation ◽  
Query Logs ◽  
High Utility

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries.

An Approach to Find Synonym and Bigram to Improve Query Recommendation Efficiency of Exploratory Search Using Search Goal Shift Graphs

2020 ◽  
Vol 17 (1) ◽  
pp. 445-450
Author(s):  
Chetana Badgujar ◽  
Vimla Jethani ◽  
Tushar Ghorpade
Keyword(s):  
Search Engine ◽  
Engine Performance ◽  
Exploratory Search ◽  
Query Recommendation

Exploratory search aids in the production of desired results which improve browsing abilities of user in current era. To improve search engine performance, this method inspects search goal shift to obtain pertinent knowledge about query entered by the user. The existing system fails to provide bigram relationship and ignores synonyms of the submitted query. Also there is un-certainty of spam links which may be included in the final result. To overcome these lacunas, the proposed framework will conduct an exploratory search to recommend query by making use of bi-gram approach which will also remove the spam links with the help of trust rank algorithm and finds all possible synonyms of the submitted query in order to obtain a proper result. This phenomenon helps to produce better user recommendations.

scholarly journals Query Recommendation for Improving Search Engine Results

2011 ◽  
Vol 1 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Hamada M. Zahera ◽  
Gamal F. El-Hady ◽  
W. F. Abd El-Wahed
Keyword(s):  
Search Engine ◽  
User Satisfaction ◽  
Real Data ◽  
Search Process ◽  
Data Sets ◽  
New Approach ◽  
Query Log ◽  
User Input ◽  
Query Recommendation ◽  
Web Contents

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

scholarly journals Query Recommendation for Optimizing the Search Engine Results

2012 ◽  
Vol 50 (13) ◽  
pp. 20-27
Author(s):  
Nikita Taneja ◽  
Rachna Chaudhary
Keyword(s):  
Search Engine ◽  
Query Recommendation

Query recommendation and its usefulness evaluation on mobile search engine

2009 ◽  
Author(s):  
Shunkai Fu ◽  
Bingfeng Pi ◽  
Michel Desmarais ◽  
Ying Zhou ◽  
Weilei Wang ◽  
...  
Keyword(s):  
Search Engine ◽  
Query Recommendation

Query ranking model for search engine query recommendation

2015 ◽  
Vol 8 (3) ◽  
pp. 1019-1038 ◽  
Author(s):  
JianGuo Wang ◽  
Joshua Zhexue Huang ◽  
Jiafeng Guo ◽  
Yanyan Lan
Keyword(s):  
Search Engine ◽  
Query Recommendation ◽  
Ranking Model
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