A combined approach for evaluating papers, authors and scientific journals

Бини, Дарио Андреа; Corso, Gianna M. Del; Romani, Francesco

doi:10.1016/j.cam.2010.02.003

Cited by 12 publications

(28 citation statements)

References 13 publications

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“…Numerical techniques have proven to be very useful to design effective algorithms in many areas related to information searching, ranking and retrieval [5,8,11,14,16,31,41]. In particular, the idea of factorizing a matrix to linearly reduce the dimensionality of the problem is a well known technique widely used in image processing [27], text mining [36], classifications [4] but also in design of effective recommender systems [7,9,25,37].…”

Section: Related Workmentioning

confidence: 99%

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Adaptive nonnegative matrix factorization and measure comparisons for recommender systems

Corso

Romani

2019

Applied Mathematics and Computation

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The Nonnegative Matrix Factorization (NMF) of the rating matrix has shown to be an effective method to tackle the recommendation problem. In this paper we propose new methods based on the NMF of the rating matrix and we compare them with some classical algorithms such as the SVD and the regularized and unregularized non-negative matrix factorization approach. In particular a new algorithm is obtained changing adaptively the function to be minimized at each step, realizing a sort of dynamic prior strategy. Another algorithm is obtained modifying the function to be minimized in the NMF formulation by enforcing the reconstruction of the unknown ratings toward a prior term. We then combine different methods obtaining two mixed strategies which turn out to be very effective in the reconstruction of missing observations. We perform a thoughtful comparison of different methods on the basis of several evaluation measures. We consider in particular rating, classification and ranking measures showing that the algorithm obtaining the best score for a given measure is in general the best also when different measures are considered, lowering the interest in designing specific evaluation measures. The algorithms have been tested on different datasets, in particular the 1M, and 10M MovieLens datasets containing ratings on movies, the Jester dataset with ranting on jokes and Amazon Fine Foods dataset with ratings on foods. The comparison of the different algorithms, shows the good performance of methods employing both an explicit and an implicit regularization scheme. Moreover we can get a boost by mixed strategies combining a fast method with a more accurate one.

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Section: Related Workmentioning

confidence: 99%

“…• NMF Is the un-regularized Nonnegative matrix factorization approach described by (5). Since we employ the Greedy Coordinate Descent algorithm described in Section 2.3 the tolerance ε becomes an input parameter as well as the number of iterations it of the ANLS scheme.…”

Section: The Tested Algorithmsmentioning

confidence: 99%

Adaptive nonnegative matrix factorization and measure comparisons for recommender systems

Corso

Romani

2019

Applied Mathematics and Computation

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“…In previous papers from the same authors [5,12,6] a model is introduced in the contest of bibliographic data which is similar to one of the models (the one we called Stiff model) of this paper. In particular in [5] an integrated model for ranking scientific publications together with authors and journals was presented.…”

Section: Motivations and Related Workmentioning

confidence: 99%

“…In particular in [5] an integrated model for ranking scientific publications together with authors and journals was presented. In that context, particular weighting strategies were implemented [6] and an exponential decay factor was introduced [12] to take into account aging of citations, i.e. the fact that if an old paper is not cited recently its importance should fade over the time.…”

Section: Motivations and Related Workmentioning

confidence: 99%

A multi-class approach for ranking graph nodes: Models and experiments with incomplete data

Corso

Romani

2016

Information Sciences

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After the phenomenal success of the PageRank algorithm, many researchers have extended the PageRank approach to ranking graphs with richer structures in addition to the simple linkage structure. Indeed, in some scenarios we have to deal with networks modeling multi-parameters data where each node has additional features and there are important relationships between such features. This paper addresses the need of a systematic approach to deal with multi-parameter data. We propose models and ranking algorithms that can be applied to a large variety of networks (bibliographic data, patent data, twitter and social data, healthcare data). We focus on several aspects not previously addressed in the literature: (1) we propose different models for ranking multi-parameters data and a class of numerical algorithms for efficiently computing the ranking score of such models, (2) we analyze stability and convergence of the proposed numerical schemes and we derive a fast and stable ranking algorithm, (3) we analyze the robustness of our models when data are incomplete. The comparison of the rank on the incomplete data with the rank on the full structure shows that our models compute consistent rankings whose correlation is up to 60% when just 10% of the links of the attributes are maintained

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“…Recently, Bini et al [1,2] and Del Corso et al [7] study some integrated models for ranking scientific publications together with authors and journals. Their models rely on certain adjacency matrices obtained from the relations of citation, authorship and publication, which combine to form a suitable irreducible stochastic matrix whose Perron vector provides the ranking.…”

Section: Introductionmentioning

confidence: 99%

On Multivariate Markov Chains for Common and Non-Common Objects in Multiple Networks

et al. 2012

NMTMA

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Node importance or centrality evaluation is an important methodology for network analysis. In this paper, we are interested in the study of objects appearing in several networks. Such common objects are important in network-network interactions via object-object interactions. The main contribution of this paper is to model multiple networks where there are some common objects in a multivariate Markov chain framework, and to develop a method for solving common and non-common objects' stationary probability distributions in the networks. The stationary probability distributions can be used to evaluate the importance of common and non-common objects via network-network interactions. Our experimental results based on examples of co-authorship of researchers in different conferences and paper citations in different categories have shown that the proposed model can provide useful information for researcher-researcher interactions in networks of different conferences and for paperpaper interactions in networks of different categories.

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A combined approach for evaluating papers, authors and scientific journals

Cited by 12 publications

References 13 publications

Adaptive nonnegative matrix factorization and measure comparisons for recommender systems

Adaptive nonnegative matrix factorization and measure comparisons for recommender systems

A multi-class approach for ranking graph nodes: Models and experiments with incomplete data

On Multivariate Markov Chains for Common and Non-Common Objects in Multiple Networks

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