Deriving Concept-Based User Profiles from Search Engine Logs

Leung, Kenneth Wai-Ting; Lee, Dik Lun

doi:10.1109/tkde.2009.144

Cited by 50 publications

(42 citation statements)

References 16 publications

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“…The authors Leung and Lee (2010) focussed on search engine personalization and developed several concept-based user profiling methods that are based on both positive and negative preferences. The proposed methods were evaluated against the previously proposed personalized query clustering method.…”

Section: The Existing Ranking Methodsmentioning

confidence: 99%

Semantic Web Improved with the Weighted IDF Feature

Gautam¹,

Kumar²

2015

ijacsa

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Abstract-The development of search engines is taking at a very fast rate. A lot of algorithms have been tried and tested. But, still the people are not getting precise results. Social networking sites are developing at tremendous rate and their growth has given birth to the new interesting problems. The social networking sites use semantic data to enhance the results. This provides us with a new perspective on how to improve the quality of information retrieval. As we are aware, many techniques of text classification are based on TFIDF algorithm. Term weighting has a significant role in classifying a text document. In this paper, firstly, we are extending the queries by "keyword+tags" instead of keywords only. In addition to this, secondly, we have developed a new ranking algorithm (JEKS algorithm) based on semantic tags from user feedback that uses CiteUlike data. The algorithm enhances the already existing semantic web by using the weighted IDF feature of the TFIDF algorithm. The suggested algorithm provides a better ranking than Google and can be viewed as a semantic web service in the domain of academics.

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Section: The Existing Ranking Methodsmentioning

confidence: 99%

Semantic Web Improved with the Weighted IDF Feature

Gautam¹,

Kumar²

2015

ijacsa

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“…• Open Directory Project (ODP) - [2]If a profile shows that a user is interested in certain categories, the search can be narrowed down by providing suggested results according to the user's preferred categories • Personalized query clustering method…”

Section: Other Methodsmentioning

confidence: 99%

Effective Personalized Web Mining by Utilizing the Most Utilized Data

Grace¹,

Maheswari²,

Nagamalai³

2011

IJDMS

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Looking into the growth of information in the web it is a very tedious process of getting the exact information the user is looking for. Many search engines generate user profile related data listing. This paper involves one such process where the rating is given to the link that the user is clicking on. Rather than avoiding the uninterested links both interested links and the uninterested links are listed. But sorted according to the weightings given to each link by the number of visit made by the particular user and the amount of time spent on the particular link.

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“…[14] investigated, compared various Text categorization algorithms and proposed profile based personalization using Open directory project. [15] proposed the search engine which took in account positive as well as negative preferences of the user. [16] proposed click content and location entropies to measure interest in the content and/or location information in the results.…”

Section: Literaturementioning

confidence: 99%

Multi Agent Architecture for Search Engine

Verma¹,

Kochar²

2016

ijacsa

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Abstract-The process of retrieving information is becoming ambiguous day by day due to huge collection of documents present on web. A single keyword produces millions of results related to given query but these results are not up to user expectations. The search results produced from traditional text search engines may be relevant or irrelevant. The underlying reason is Web documents are HTML documents that do not contain semantic descriptors and annotations.The paper proposes multi agent architecture to produce fewer but personalized results. The purpose of the research is to provide platform for domain specific personalized search. Personalized search allows delivering web pages in accordance with user's interest and domain. The proposed architecture uses client side as well server side personalization to provide user with personalized fever but more accurate results. Multi agent search engine architecture uses the concept of semantic descriptors for acquiring knowledge about given domain and leading to personalized search results. Semantic descriptors are represented as network graph that holds relationship between given problem in form of hierarchy. This hierarchical classification is termed as Taxonomy.

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Deriving Concept-Based User Profiles from Search Engine Logs

Cited by 50 publications

References 16 publications

Semantic Web Improved with the Weighted IDF Feature

Semantic Web Improved with the Weighted IDF Feature

Effective Personalized Web Mining by Utilizing the Most Utilized Data

Multi Agent Architecture for Search Engine

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