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Journal ArticleDOI

Prediction of user interest fluctuation using fuzzy neural networks in web search

12 Jun 2020-Vol. 8, Iss: 4, pp 307-319
TL;DR: F fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios and the future needs of users are categorized using this proposed system.
Abstract: The user interest in content searching in the web will be changed over by time.,The system is in need to find the content of user over the temporal aspects.,So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.,So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.,In this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.,In this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.,This work also categorizes the future needs of users using this proposed system.
Citations
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Journal ArticleDOI
TL;DR: This work proposes a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching and compares the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e).
Abstract: Web search querying is an inevitable activity of any Internet user. The web search engine (WSE) is the easiest way to search and retrieve data from the Internet. The WSE stores the user’s search queries to retrieve the personalized search result in a form of query log. A user often leaves digital traces and sensitive information in the query log. WSE is known to sell the query log to a third party to generate revenue. However, the release of the query log can compromise the security and privacy of a user. In this work, we propose a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching. PaOSLo systematically groups users based on profile similarity. The primary objective of this work is to evaluate the impact of the systematic group compared to random grouping. We first computed the similarity between the users’ profiles and then clustered them using the K-mean algorithm to group the users systematically. Unlikability and indistinguishability are the two dimensions in which we have measured the privacy of a user. To compute the impact of systematic grouping on a user’s privacy, we have experimented with and compared the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e). Results show that, at the top degree of the ODP hierarchy, PaOSLo preserved 10% and 3% better profile privacy than the modern distributed protocols mentioned above. In addition, the PaOSLo has less profile exposure for any group size and at each degree of the ODP hierarchy.

5 citations

References
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Journal ArticleDOI
TL;DR: A common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors, providing competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.
Abstract: Link prediction in complex networks has attracted much attention in many fields. In this paper, a common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors. Numerical experiments on six real networks demonstrated the high effectiveness and efficiency of the new index compared with five well-known and widely accepted indices: the common neighbors, resource allocation index, preferential attachment index, local path index and Katz index. The new index provides competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.

36 citations

Journal ArticleDOI
TL;DR: A new approach is proposed to infer user interests based on their queries and fast profile logs and to provide relevant information to users based on personalization to provide most accurate and relevant contents to the users when compared with other related work.
Abstract: Predicting user interest based on their browsing pattern is useful in relevant information retrieval. In such a scenario, queries must be unambiguous and precise. For a broad-topic and ambiguous query, different users may with different interests may search for information from the internet. The inference and analysis of user search goals using rules will be helpful to enhance the relevancy and user experience. A major deficiency of generic search system is that they have static model which is to be applied for all the users and hence are not adaptable to individual users. User interest is important when performing clustering so that it is possible to enhance the personalization. In this paper, a new approach is proposed to infer user interests based on their queries and fast profile logs and to provide relevant information to users based on personalization. For this purpose, a framework is designed to analyze different user profiles and interests while query processing including relevance analysis. Implicit Feedback sessions are also constructed from user profiles based on mouse and button clicks made in their current and past queries. In addition, browsing behaviors of users are analyzed using rules and also using the feedback sessions. Temporary documents are generated in this work for representing the feedback sessions effectively. Finally, personalization is made based on browsing behavior and relevant information is provided to the users. From the experiments conducted in this work, it is observed that the proposed model provide most accurate and relevant contents to the users when compared with other related work.

28 citations

Journal ArticleDOI
01 Apr 2018
TL;DR: This work has made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents, using linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X.
Abstract: Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains subjective information and the kind of subjective information it expresses. The subjective information represents the attitude behind the text: positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great concern. We have made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents. We use linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. We conduct a series of experiments to test the performance of the system.

20 citations

Proceedings ArticleDOI
01 Aug 2016
TL;DR: A series of experimental results show that the method of internet public opinion trend prediction based on collaborative filtering can effectively predict the development trend ofinternet public opinion.
Abstract: Collaborative filtering recommendation has very important applications in the personalized recommendation. Especially it is widely used in e-commerce. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations and provide a lot similar or interesting advice for users. The method of internet public opinion trend prediction based on collaborative filtering is proposed in order to solve the problem of internet public opinion trend prediction. This paper introduces the collaborative filtering algorithm and study user-based collaborative filtering algorithm, then the principles of internet public opinion trend prediction based on collaborative filtering are analyzed, and the frame structure of internet public opinion trend prediction is designed. Furthermore, a series of experimental results show that this method can effectively predict the development trend of internet public opinion.

17 citations

Proceedings ArticleDOI
01 Feb 2017
TL;DR: A novel approach based on the semi-supervised word alignment model (SWAM), which identifies the relations among the words in a sentence, is proposed, a graph-based algorithm where target opinion words are compared with the other opinion word and extract the long span relations between the words.
Abstract: In the fast moving world, peoples want to reduce the shopping time and purchase their needed products through online. In addition, online shopping provides the product reviews and helps the customer to get the better among the variety of brands. In this, mining the opinion words and the polarity of the reviews are the important task to detect the exact opinion of the customer reviews. In this paper, a novel approach is proposed based on the semi-supervised word alignment model (SWAM), which identifies the relations among the words in a sentence. It's a graph-based algorithm where target opinion words are compared with the other opinion word and extract the long span relations among the words. Unlike, syntax based method, this proposed model reduce the parsing errors by dealing with informal online texts. The mined reviews of this proposed system provides better precision when compared with standard unsupervised alignment review models. The experimental results show that this approach effectively mining the user reviews and provide the better recommendation.

16 citations