Predicting who rated what in large-scale datasets

Yan, Liu; Kou, Zhenzhen

doi:10.1145/1345448.1345462

Cited by 30 publications

(13 citation statements)

References 4 publications

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“…In other words, it is important to first predict who is likely to rate what before focusing on the ratings. This was a specific task in KDD Cup 2007 [Liu and Kou 2007]. More recent models by Koren [Koren 2009] explicitly account for changes in user preferences over time.…”

Section: Related Workmentioning

confidence: 99%

“…This problem has been considered in a variety of contexts [Hasan et al 2006;Liben-Nowell and Kleinberg 2007;Sarkar et al 2007]. Collaborative filtering is also a related task, where the objective is to predict interest of users to objects (movies, books, music) based on the interests of similar users [Liu and Kou 2007;Koren et al 2009]. The temporal link prediction problem is different from missing link prediction, which has no temporal aspect and where the goal is to predict missing connections in order to describe a more complete picture of the overall link structure in the data [Clauset et al 2008].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Link Prediction Using Matrix and Tensor Factorizations

Dunlavy

Kolda

Acar

2011

ACM Trans. Knowl. Discov. Data

434

260

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The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for times 1 through T , can we predict the links at time T + 1? If our data has underlying periodic structure, can we predict out even further in time, i.e., links at time T + 2, T + 3, etc.? In this paper, we consider bipartite graphs that evolve over time and consider matrix-and tensor-based methods for predicting future links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix-and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem. Additionally, we show that tensor-based techniques are particularly effective for temporal data with varying periodic patterns.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Link Prediction Using Matrix and Tensor Factorizations

Dunlavy

Kolda

Acar

2011

ACM Trans. Knowl. Discov. Data

434

260

View full text Add to dashboard Cite

show abstract

“…It has beeen used in the analysis of the internet [1,27], social networks [2], and biological networks [3,11]. It has also been used for designing recommendation systems [16,18] and classification systems [12]. See the survey of Al Hasan and Zaki [4] for more applications and an introduction to the various techniques used in link prediction.…”

Section: Link Predictionmentioning

confidence: 99%

Handling Oversampling in Dynamic Networks Using Link Prediction

Fish

Caceres

2015

Machine Learning and Knowledge Discovery in Databases

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Abstract. Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network given previous snapshots. We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling. We also introduce a novel generative model of noise in dynamic networks that represents oversampling. We demonstrate the results of our approach on both synthetic and real-world data.

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“…In the area of web science and Internet, it can be used for automatic web hyperlink creation [1] and predicting website hyperlinks [2]. In e-commerce, link prediction is used for building recommender systems [3,4,5]. It can also be used for various applications in other scientific disciplines.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Link Prediction Problem in Social Networks

Kumar¹

2017

IJRASET

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Link prediction is a crucial task in analysis of online social networks. It can be used for friend recommendations in social networks. It also has applications in other areas like bioinformatics, information retrieval and e-commerce. Currently, variety of techniques is available to deal with link prediction problem, ranging from local feature based approaches to global feature based approaches. Some recent techniques combine the advantages of these two approaches and comes under hybrid category. all these methods differ from each other with respect to prediction performance in terms of accuracy, efficiency, and generalization ability. In this paper, we survey some representative link prediction techniques under each category. We largely consider three types of approaches. First category is of local feature based approaches which do not exploit the whole network structure. Second category is of global feature based approaches which use the overall path structure of a network. And, finally the hybrid approaches which combine the advantages of above two approaches (local and global) in computation of similarity scores between each pair of nodes in a network. We discuss some recent existing approaches corresponding to these broad categories and analyze their strength and weaknesses. We conclude the paper with discussion on recent developments and future research direction.

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Predicting who rated what in large-scale datasets

Cited by 30 publications

References 4 publications

Temporal Link Prediction Using Matrix and Tensor Factorizations

Temporal Link Prediction Using Matrix and Tensor Factorizations

Handling Oversampling in Dynamic Networks Using Link Prediction

A Survey on Link Prediction Problem in Social Networks

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