Asymptotic analysis for personalized Web search

Personalized PageRank is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equation $R\stackrel{d}{=}\sum_{i=1}^NA_iR_i+B$, where $R_i$'s are distributed as $R$. This equation is inspired by the original definition of the PageRank. In particular, $N$ models the number of incoming links of a page, and $B$ stays for the user preference. Assuming that $N$ or $B$ are heavy-tailed, we employ the theory of regular variation to obtain the asymptotic behavior of $R$ under quite general assumptions on the involved random variables. Our theoretical predictions show a good agreement with experimental data

Using thematic ontologies for user- and group- based adaptive personalization in web searching

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents

Personalized Web Search Using Browsing History and Domain Knowledge Based on Enhanced User Profile

Generic search engines are important for retrieving relevant information from web. However these engines follow the "one size fits all" model which is not adaptable to individual users. Personalized web search is an important field for tuning the traditional IR system for focused information retrieval. This paper is an attempt to improve personalized web search. User's Profile provides an important input for performing personalized web search. This paper proposes a framework for constructing an Enhanced User Profile by using user's browsing history and enriching it using domain knowledge. This Enhanced User Profile can be used for improving the performance of personalized web search. In this paper we have used the Enhanced User Profile specifically for suggesting relevant pages to the user. The experimental results show that the suggestions provided to the user using Enhanced User Profile ae better than those obtained by using a User Profile

Provision of Relevant Results on web search Based on Browsing History

Different users submit a query to a web search engine with different needs. The general type of search engines follows the "one size fits all" model which is not flexible to individual users resulting in too many answers for the query.  In order to overcome this drawback, in this paper, we propose a framework for personalized web search which considers individual's interest introducing intelligence into the traditional web search and producing only relevant pages of user interest. This proposed method is simple and efficient which ensures quality suggestions as well as promises for effective and relevant information retrieval. The framework for personalized web search engine is based on user past browsing history. This context is then used to make the web search more personalized. The results are encouraging

A Novel Framework For User Customizable Privacy Preserving Search

The objective of the Personalized web search (PWS) is to provide an effective and efficient search results, which are tailor mode for individual user needs. we build user profiles based on user preference and these profiles are then used to re-rank the search results and rank the order of user-examined results.User privacy can be protected without affecting the personalized search quality. However, users are troubled, with exposing personal preference information to search engines has become a major limitation for profile based personalized web search.The Privacy-preserving personalized web search framework is called UPS framework which can generalize profiles for each query according to user-specific privacy requirements. .In general, there is a tradeoff between the search quality and the level of privacy protection achieved from generalization. Effective generalization algorithms namely GreedyDP and GreedyIL are used to support the runtime profiling. Experiments are conducted on real web search data show that the algorithms are effective in enhancing the stability of the search quality and avoids the unnecessary exposure of the user profile. DOI: 10.17762/ijritcc2321-8169.150313

Data Mining in Personalized Web Searching Data�s

World Wide Web (WWW) is very popular and commonly used internet�s information retrieval service. Nowa-days commonly used task on internet is web search. User gets variety of related information for their queries. To provide more relevant and effective results to user, Personalization technique is used. Personalized web search refer to search information that is tailored specifically to a person�s interests by incorporating information about query provided. Two general types of approaches to personalizing search results are modifying user�s query and reranking search results. Several personalized web search techniques based on web contents, web link structure, browsing history ,user profiles and user queries. The proposed paper is to represent survey on various techniques of personalization

Intelligent personalized approaches for semantic search and query expansion

University of Technology Sydney. Faculty of Engineering and Information Technology.In today’s highly advanced technological world, the Internet has taken over all aspects of human life. Many services are advertised and provided to the users through online channels. The user looks for services and obtains them through different search engines. To obtain the best results that meet the needs and requirements of the users, researchers have extensively studied methods such as different personalization methods by which to improve the performance and efficiency of the retrieval process. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Recently, semantic techniques have had a significant importance in the field of personalized querying and personalized web search engines. This thesis focuses on both processes of personalized web search engines, first the reformulation of queries and second ranking query results. The importance of personalized web search lies in its ability to identify users' interests based on their personal profiles. This work contributes to personalized web search services in three aspects. These contributions can be summarized as follows: First, it creates user profiles based on a user’s browsing behaviour, as well as the semantic knowledge of a domain ontology, aiming to improve the quality of the search results. However, it is not easy to acquire personalized web search results, hence one of the problems that is encountered in this approach is how to get a precise representation of the user interests, as well as how to use it to find search results. The second contribution builds on the first contribution. A personalized web search approach is introduced by integrating user context history into the information retrieval process. This integration process aims to provide search results that meet the user’s needs. It also aims to create contextual profiles for the user based on several basic factors: user temporal behaviour during browsing, semantic knowledge of a specific domain ontology, as well as an algorithm based on re-ranking the search results. The previous solutions were related to the re-ranking of the returned search results to match the user’s requirements. The third contribution includes a comparison of three-term weight methods in personalized query expansion. This model has been built to incorporate both latent semantics and weighting terms. Experiments conducted in the real world to evaluate the proposed personalized web search approach; show promising results in the quality of reformulation and re-ranking processes compared to Google engine techniques