Enhancing personalized web search re-ranking algorithm by incorporating user profile

Veningston, K.; Shanmugalakshmi, R.

doi:10.1109/icccnt.2012.6396036

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…Higher the in edges or out edges, higher the rank is assigned to link. But from our observation, this phenomenon might not be good for some links as shown There are so many research has been done to personalize web search result [1], [2], [3], [4], [5], [6], [7], [8] that incorporate user profile by analyzing previous search history of specific user. But still problem of current personalized search approach is that the major approaches are uniformly applied to all users and queries [9] and there are not fit to all.…”

Section: Introductionmentioning

confidence: 92%

“…Some of personalized search approaches in [2], [8], [11], [13]have used lists of keywords to represent user interests and built user preferences as vectors of distinct terms and constructed them by aggregating past preferences, including both long-term, short-term preferences, query logs, bookmarks etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, in [1], [2], [3], [10], [11] user profile is generated from large query log. In proposed system, we have generated the user profile from web log data.…”

Section: A Generation Of 3-level Semi-structured Web Log File For Usmentioning

confidence: 99%

“…It argued that queries should not be handled in the same general manner and expectation of user will be changed over a time on same query. It has been shown in [2] for some navigational queries like Google, Yahoo, YouTube, etc. there is no need to personalize web search results because users normally use uniform links from retrieved results.…”

Section: Introductionmentioning

confidence: 98%

“…In offline phase user profile is generated from web-log file. [1], [2], [3], [9], [10], [13] and [14] had use query logs for incorporating user profile but query log alone is not enough to determine exact context of any user [11], so in this work we have used web-log file in semi-structured form that stores user's previous search snippets to infer user interest. When user enters into the system web-log file of associated user is analyzed and knowledge base has been generated.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach to personalize web search through user profiling and query reformulation

Makvana

Shah

2014

2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)

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with a inundating of information in WWW (World Wide Web) users are often failed to retrieve search result in context of their interest through existing search engines. So the personalization of web search result has to be carryout that process user's query and re-rank retrieved results based on their interest. User have diverse background on same query, it is very difficult for some informative query to identify user's current intention. In this paper, a novel approach is proposed that personalize web search result through query reformulation and user profiling.First,a framework is proposed that identify relevant search term for particular user from previous search history by analysing web log file maintained in the server. These terms are appended to user's ambiguous query. Second, the proposed approach proceeds the user's search result and re-rank the retrieved result by identifying interest value of user on retrieved links. Proposed new approach also identify user interest on retrieved links by combing the user interest value generated from VSM (Vector Space Model) and actual rank of that link. Third, the framework also suggest some keywords that help to incorporate user's current interest. Finally, experimental result shows the effectiveness of proposed search engine with commercial search engine with different criteria.

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Section: Introductionmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, in [1], [2], [3], [10], [11] user profile is generated from large query log. In proposed system, we have generated the user profile from web log data.…”

Section: A Generation Of 3-level Semi-structured Web Log File For Usmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%