Efficient link-based similarity search in web networks

Zhang, Mingxi; Hu, Hao; He, Zhenying; Gao, Liping; Liujie, Sun

doi:10.1016/j.eswa.2015.07.042

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…The partial index used for storing the partial similarity scores in order to reduce the candidate size and optimize similarity computation. The spiritual of the partial index is similar to the pruning index proposed in our previous work in [57, 58]. An example of partial index is shown as Fig 1, where TermID denotes the term ID, DocID denotes document ID, PartialSim denotes the partial similarity, and the two-tuple 〈DocID,PartialSim〉 describes that the partial similarity between a document DocID and a term TermID that the document DocID belongs to is PartialSim.…”

Section: Methodsmentioning

confidence: 90%

An index-based algorithm for fast on-line query processing of latent semantic analysis

Zhang

Wang

2017

PLoS ONE

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Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.

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Section: Methodsmentioning

confidence: 90%

An index-based algorithm for fast on-line query processing of latent semantic analysis

Zhang

Wang

2017

PLoS ONE

Self Cite

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“…Further more, pairwise vertex similarity is a fundamental index for many network functions and physical systems [53,54]. That is to say, the proposed similarity index can find applications in solving many network-related problems, such as link predication [55,56], community detection [57,58,59], spreading activation [60], network evolution [61,62], web searching [63,64], data clustering [65,66], and gene ranking [67]. By contrast, it would be hard for the hybrid transition matrix to be applied to solve these problems.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A vertex similarity index for better personalized recommendation

Chen

Zhang

Lü

et al. 2017

Physica A: Statistical Mechanics and its Applications

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Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index.

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“…With the student-book network becoming large, the computation of SimRank would be expensive in terms of time and space cost. Fortunately, there are extensive optimization techniques on SimRank computation in previous work [19][20][21][22][23][24], which significantly reduced the computation cost. For example, in our previous research [24], the reduction of the time and space cost of the iterative SimRank computation is on average 99.83%, accuracy loss is on average 0.02% NDCG, which can be used to optimize the similarity computation in student-book network.…”

Section: Similarity Between Booksmentioning

confidence: 99%

“…A. McCann [23] modified SimRank to compute the similarities for partial object pairs, which is important when only the similarities of partial object pairs are required in some applications. M. Zhang and H. Hu [24] proposed WebSim that reduces the computation cost of similarity search by limiting the iteration number into two, and uses a partial index to reduce the execution time of on-line query processing. These approaches can be easily taken into student-book network for speeding up the similarity computation between books.…”

Section: Related Workmentioning

confidence: 99%

Searching Similar Books Based on Student’s Preference for Personalized Education

Zhang¹

2017

SJEDU

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Abstract:Personalized education aims to give students a personalized learning schedule according to students' backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student's preference for personalized education. We build the student-book network based on the students' ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student's personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students' query intentions.

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Efficient link-based similarity search in web networks

Cited by 9 publications

References 24 publications

An index-based algorithm for fast on-line query processing of latent semantic analysis

An index-based algorithm for fast on-line query processing of latent semantic analysis

A vertex similarity index for better personalized recommendation

Searching Similar Books Based on Student’s Preference for Personalized Education

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