Exploring Web Search Behavior Patterns to Personalize the Search Results

Romsaiyud, Walisa; Premchaiswadi, Wichian

doi:10.1109/incos.2011.91

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…This method constructs the recommendation tree for furnishing the Web Pages. Romsaiyud et al, [13] have amalgamated Social Tagging and Collaborative Web Search for enhancing the recommendation of Web Pages.Agarwal et al, [14] have proposed a methodology that incorporates fusion of ranks from the results of Multiple Results where a tool is developed to aggregate several search engines and the query is sent to all search engines. In Rank Fusion, the rank is assigned to the search engines and the best results are reordered before they are presented to the user.…”

Section: Related Workmentioning

confidence: 99%

A differential semantic algorithm for query relevant web page recommendation

Deepak

Priyadarshini

Babu

2016

2016 IEEE International Conference on Advances in Computer Applications (ICACA)

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With the exponential rise in the amount of information in the World Wide Web, there is a need for a much efficient algorithm for Web Search. The traditional keyword matching as well as the standard statistical techniques is insufficient as the Web Pages they recommend are not highly relevant to the query. With the growth in Semantic Web, an algorithm which semantically computes the most relevant Web Pages is required. In this paper, a methodology which computes the semantic heterogeneity between the keywords, content words and query words for web page recommendation is incorporated. A Differential Adaptive PMI Algorithm is formulated for with varied thresholds for recommending the Web Pages based on the input query. The proposed methodology yields an accuracy of 0.87 which is much better than the existing strategies.

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Section: Related Workmentioning

confidence: 99%

A differential semantic algorithm for query relevant web page recommendation

Deepak

Priyadarshini

Babu

2016

2016 IEEE International Conference on Advances in Computer Applications (ICACA)

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“…The fourth type of search engines is the meta search engines. These are search engines that fetch results from other search engines and display the results on their result pages [16]. These types of search engines therefore fetches results from other types of search engines, combine the results then rank the result before displaying the result on their result pages [9].…”

Section: IIImentioning

confidence: 99%

Intelligent metadata web search engines: A brief review of literature on intelligent metadata based search engines

Mahdi

Ahmad

Ismail

2014

Proceedings of the 6th International Conference on Information Technology and Multimedia

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Search engines are vital in the current digital world. Given the huge amount of information on the internet, search engines are vital tools that internet users are using to search web pages for the required information. However, most of the search engines currently in the market are inadequate and thus do not completely serve the needs of internet users. This is because in most cases they give results that are inaccurate and irrelevant. It is thus important to deploy search engine solutions that are embedded with intelligent agents that enable the search engines to work with enormous amount of information and give the search engines the capability to learn from their environment in order to give accurate and relevant results.

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“…The fourth type of search engines is the Meta search engines. These are search engines that fetch results from other search engines and display the results on their result pages [14]. These types of search engines therefore fetches results from other types of search engines, combine the results then rank the result before displaying the result on their result pages [7].…”

Section: Fig 1: the Workings Of A Crawler Based Search Engine [7]mentioning

confidence: 99%

A Visual Exploratory Meta Data Search Engine Solution based on Cloud Computing

NajahMahdi¹,

Ahmad²,

Ismail³

2014

IJCA

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With the rapid upsurge of web use and internet connectivity (Access), the traditional search engines face significant challenges in the contemporary searches, response speed and the storage of bulk pages. The search engine contained in the cloud can help alleviate the identified shortcomings. This is simply because cloud computing is advantageous in mass data processing and storage. Through an analysis of a visual exploratory Meta data search engine on a cloud platform, there will be strategies adopted to optimize the search engine in order to improve the overall performance and effectiveness of search.

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Exploring Web Search Behavior Patterns to Personalize the Search Results

Cited by 4 publications

References 24 publications

A differential semantic algorithm for query relevant web page recommendation

A differential semantic algorithm for query relevant web page recommendation

Intelligent metadata web search engines: A brief review of literature on intelligent metadata based search engines

A Visual Exploratory Meta Data Search Engine Solution based on Cloud Computing

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