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SimRank

About: SimRank is a research topic. Over the lifetime, 250 publications have been published within this topic receiving 21163 citations.


Papers
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Journal ArticleDOI
TL;DR: A page rank mechanism called Hybrid Page Rank Algorithm is proposed which is based on both content and link structure of the web pages which is used to find more relevant information according to user’s query.
Abstract: As the web is escalating day by day, so the most concerned issue for the users would be how to collect the useful information and to find their genuine information effectively and quickly. With the tremendous growth of information available to end users through the web, search engines play a vital role in retrieving and organizing relevant data for various purposes. The ranking of the web pages for the web search engine is one of the significant problems at present. This leads to the important attention to the research community. In this paper, a page rank mechanism called Hybrid Page Rank Algorithm is proposed which is based on both content and link structure of the web pages. This algorithm is used to find more relevant information according to user’s query. This paper also presents the comparison between SimRank Algorithm and the Hybrid Page Rank Algorithm.

2 citations

Journal Article
TL;DR: This paper first extracts product feature expressions and sentimental words in pairs to build a bipartite graph, and then adopts the Weight Normalized SimRank to compute similarity between different feature expressions in the bipartites, and finally optimizes the Bayesian classifier in Semi-Supervised Learning via the similarity.
Abstract: This paper focuses on clustering different feature expressions in product reviews into proper groups.In product reviews,the same features may have different expressions,e.g."appearance" and "design" of a mobile phone actuallyindicate the same feature.Considering the fact that different expressions are always used with same sentimental words in a sentence,this paper first extracts product feature expressions and sentimental words in pairs to build a bipartite graph,and then adopts the Weight Normalized SimRank to compute similarity between different feature expressions in the bipartite graph,and finally optimizes the Bayesian classifier in Semi-Supervised Learning via the similarity.Experimental results show that the proposed method is valid.

2 citations

Proceedings ArticleDOI
16 Jul 2010
TL;DR: This paper proposes a new method to cluster law texts based on referential relation of laws and applies SimRank algorithm in the domain of Law and uses it to carry out text clustering.
Abstract: This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity’s similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank algorithm in the domain of Law and use it to carry out text clustering. Prototype and experiments show that our solution is feasible. We also publish the extracted data as Linked Law Data with RDF data model, which forms the first open semantic web database in Law domain. Linked Law Data enables user to access law data with rich data links and query web data by application interface of Semantic Web.

2 citations

Book ChapterDOI
16 Jun 2014
TL;DR: This paper presents various similarity methods and evaluates their effectiveness via extensive experiments on a real-world dataset of scientific papers.
Abstract: Similarity computation for academic literature data is one of the interesting topics that have been discussed recently in information retrieval and data mining. Consequently, a variety of methods has been proposed to compute the similarity of scientific papers. In this paper, we present various similarity methods and evaluate their effectiveness via extensive experiments on a real-world dataset of scientific papers.

2 citations

Journal ArticleDOI
TL;DR: The experiments have shown that OmniRank outperforms its comparison partners in terms of effectiveness and recommendation accuracy, because it exploits information on both multi-step and omni-directional neighborhoods (unipartite and bipartite).
Abstract: In this paper, we propose a new node similarity measure, OmniRank, for multi-dimensional and heterogeneous social networks. In particular, we recursively propagate the structural similarity computation beyond the neighborhood of the nodes to the entire heterogeneous (e.g., user, item, tag) graph, which incorporates several unipartite and bipartite graphs. We have evaluated experimentally OmniRank and compared it against other state-of-the-art algorithms (wRWR, SimRank and P-Rank) on two real-life data sets (HetRec 2011 and GeoSocialRec). Our experiments have shown that OmniRank outperforms its comparison partners in terms of effectiveness and recommendation accuracy, because it exploits information on both multi-step and omni-directional neighborhoods (unipartite and bipartite).

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202115
202026
201916
201817
201719
201616