Topic
SimRank
About: SimRank is a research topic. Over the lifetime, 250 publications have been published within this topic receiving 21163 citations.
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30 Oct 2010
TL;DR: A weight model for tagging systems that considers the user dimension unlike existing measures based on tag frequency is proposed, which achieves 20% improvement over the traditional similarity measures like dice and cosine similarity and also over the most recent tag similarity measureslike mutual information with distributional aggregation.
Abstract: The problem of measuring semantic relatedness between social tags remains largely open. Given the structure of social bookmarking systems, similarity measures need to be addressed from a social bookmarking systems perspective. We address the fundamental problem of weight model for tags over which every similarity measure is based. We propose a weight model for tagging systems that considers the user dimension unlike existing measures based on tag frequency. Visual analysis of tag clouds depicts that the proposed model provides intuitively better scores for weights than tag frequency. We also propose weighted similarity model that is conceptually different from the contemporary frequency based similarity measures. Based on the weighted similarity model, we present weighted variations of several existing measures like Dice and Cosine similarity measures. We evaluate the proposed similarity model using Spearman's correlation coefficient, with WordNet as the gold standard. Our method achieves 20% improvement over the traditional similarity measures like dice and cosine similarity and also over the most recent tag similarity measures like mutual information with distributional aggregation. Finally, we show the practical effectiveness of the proposed weighted similarity measures by performing search over tagged documents using Social SimRank over a large real world dataset.
21 citations
13 Apr 2015
TL;DR: This paper proposes a scalable approximation algorithm with an arbitrary accuracy for the similarity join problem with the SimRank similarity measure that scales up to the network of 5M vertices and 70M edges.
Abstract: Similarity join finds all pairs of objects (i, j) with similarity score s(i, j) greater than some specified threshold θ. This is a fundamental query problem in the database research community, and is used in many practical applications, such as duplicate detection, merge/purge, record linkage, object matching, and reference conciliation.
21 citations
25 Jul 2011
TL;DR: A method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates for online dating networks can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.
Abstract: A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.
21 citations
16 Apr 2018
TL;DR: This paper proposes a novel local push based algorithm for computing all-pairs SimRank and shows that its algorithms outperform the state-of-the-art static and dynamic all-Pair SimRank algorithms.
Abstract: SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the Sim-Rank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing all-pairs SimRank. We show that our algorithms outperform the state-of-the-art static and dynamic all-pairs SimRank algorithms.
20 citations
26 Apr 2010
TL;DR: An algorithm called SimLearn is proposed to extend MoK-SimRank to heterogeneous image-rich information network, and account for both link-based and content-based similarities by seamlessly integrating reinforcement learning with feature learning.
Abstract: In this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduce MoK-SimRank to significantly improve the speed of SimRank, one of the most popular algorithms for computing node similarity in information networks. Next, we propose an algorithm called SimLearn to (1) extend MoK-SimRank to heterogeneous image-rich information network, and (2) account for both link-based and content-based similarities by seamlessly integrating reinforcement learning with feature learning.
20 citations