Add to ORKGIn this paper, we propose an automated solution for ranking query results of Web databases in an user- and query-dependent environment. We first propose a learning method for inferring a workload of ranking functions by investigating users ’ browsing choices over individual query results. Based on this workload, we propose a similarity model, based on two novel metrics – user- and query-similarity, for ranking query results when user browsing choices are not available. We present the results of an experimental study that validates our proposal for user-and query-dependent ranking
We address the problem how to select the correct answers to a query from among the partially incorre...
We explore the potential of using users click-through logs where no editorial judgment is available ...
We present a framework for discovering sets of web queries having similar latent needs, called searc...
The information available in the World Wide Web is stored using many real Web databases (e.g. vehicl...
Ranking is the central problem for information retrieval (IR), and employing machine learning techni...
Ranking is the central problem for information retrieval (IR), and employing machine learning techni...
Data retrieval finding relevant data from large databases - has become a serious problem as myriad d...
The growing importance and need of data processing for information extraction is vital for Web datab...
Numerous clients seeking web databases in areas, for example, vehicles, property and so forth has tu...
Many users searching databases through the web in various domains like vehicles, real estate, etc. O...
In query based Web search, a significant percentage of user queries are underspecified, most likely ...
Web search engines are increasingly deploying many features, combined using learning to rank techniq...
Web search ranking models are learned from features origi-nated from different views or perspectives...
Many web databases are only accessible through a proprietary search interface which allows users to ...
Effective ranking functions are an essential part of commercial search engines. We focus on developi...
We address the problem how to select the correct answers to a query from among the partially incorre...
We explore the potential of using users click-through logs where no editorial judgment is available ...
We present a framework for discovering sets of web queries having similar latent needs, called searc...
The information available in the World Wide Web is stored using many real Web databases (e.g. vehicl...
Ranking is the central problem for information retrieval (IR), and employing machine learning techni...
Ranking is the central problem for information retrieval (IR), and employing machine learning techni...
Data retrieval finding relevant data from large databases - has become a serious problem as myriad d...
The growing importance and need of data processing for information extraction is vital for Web datab...
Numerous clients seeking web databases in areas, for example, vehicles, property and so forth has tu...
Many users searching databases through the web in various domains like vehicles, real estate, etc. O...
In query based Web search, a significant percentage of user queries are underspecified, most likely ...
Web search engines are increasingly deploying many features, combined using learning to rank techniq...
Web search ranking models are learned from features origi-nated from different views or perspectives...
Many web databases are only accessible through a proprietary search interface which allows users to ...
Effective ranking functions are an essential part of commercial search engines. We focus on developi...
We address the problem how to select the correct answers to a query from among the partially incorre...
We explore the potential of using users click-through logs where no editorial judgment is available ...
We present a framework for discovering sets of web queries having similar latent needs, called searc...