A User Model Based Ranking Method of Query Results of Meta-Search Engines

Lu, Yan; Li, Yuanyi; Xu, Meng; Hu, Weihui

doi:10.1109/icnisc.2015.123

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In the recent times, researchers have tried to build algorithms that are more adaptable to the present-day scenario with dynamic data sets and also nowadays giving out results according to the user's choice particularly personalized according to the user is the current demand. To meet these requirements we have Modified Bayesian [6] and User Model Based Ranking [14]. The Bayesian method, which was earlier used for training sets and with their local ranks, is now modified according to the merging strategy without a training data set.…”

Section: Related Workmentioning

confidence: 99%

A Study of Various Result Merging Strategies for a Meta Search Engine

Sathiya¹,

Jayasree²,

Tangirala³

et al. 2018

IJET

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As the amount of data is growing day by day, the sources for these data are also growing simultaneously and to search through this very data, we need the use of search engines. Since each search engine is limited to its confined set of data, it would be even better to make use of a Meta search engine which will give us more relevant results than the ones obtained from any single search engine. It acts as an interface that provides the user with a single view from the various underlying search engines. The data is collected from these underlying search engines after they are accessed with the processed query from the Meta search engine. The collected data is merged using an algorithm and the algorithm will be a major factor in giving the best possible results. In this paper, we are going to discuss about the various existing metasearch engines and the different merging techniques and their approaches.

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Section: Related Workmentioning

confidence: 99%

A Study of Various Result Merging Strategies for a Meta Search Engine

Sathiya¹,

Jayasree²,

Tangirala³

et al. 2018

IJET

View full text Add to dashboard Cite

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“…Lu et al [18] proposed a user model based ranking method, in which the user model is mainly used to capture and record the user's interests. Wang et al [19] proposed a general ranking model adaptation framework for personalised search using a user-independent ranking model and the number of adaptation queries from individual users.…”

Section: Content-based Rankingmentioning

confidence: 99%

Re-ranking Google search returned web documents using document classification scores

Plansangket

Gan

2016

AIR

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Web document ranking is a very challenging issue for search engines because about 80% of the search engine users are usually interested in the top three returned search results only. This paper proposes an effective method for re-ranking Google search returned web documents/pages based on document classification. This method downgrades some web documents/pages that have lower classification scores or been classified into categories irrelevant to the query. The experimental results show that the re-ranking of Google search returned web documents using document classification scores can significantly improve the ranking performance in terms of the integrated evaluation result using three criteria: MAP, nDCG, and P@20. It is evident that the proposed re-ranking method can meet the user's information need better.

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