Coupled Behavior Analysis with Applications

Cao, Longbing; Ou, Yuming; Yu, Philip S.

doi:10.1109/tkde.2011.129

Cited by 142 publications

(155 citation statements)

References 28 publications

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“…Generally, content-based methods are able to cope with the sparsity problem, however, they often assume an item's attributes are "iid" which does not always hold in reality. Actually, several research outcomes (Cao, Ou, and Yu 2012) ) (Wang, She, and Cao 2013a) (Wang, She, and Cao 2013b) have been proposed to handle these challenging issues. To the best of our knowledge, in relation to RS, there is only one paper (Yu et al 2013) which applies a coupled clustering method to group the items, then exploits CF to make recommendations.…”

Section: Content-based Methodsmentioning

confidence: 99%

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Coupled Item-Based Matrix Factorization

Cao

2014

Web Information Systems Engineering – WISE 2014

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The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary information. However, most of the existing methods for inferring the relationships between items assume that the attributes are "independently and identically distributed (iid)", which does not always hold in reality. In fact, the attributes are more or less coupled with each other by some implicit relationships. Therefore, in this paper we propose an attribute-based coupled similarity measure to capture the implicit relationships between items. We then integrate the implicit item coupling into MF to form the Coupled Item-based Matrix Factorization (CIMF) model. Experimental results on two open data sets demonstrate that CIMF outperforms the benchmark methods.

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Section: Content-based Methodsmentioning

confidence: 99%

“…Collaborative filtering (CF) (Su and Khoshgoftaar 2009) (Sarwar et al 2001) (Deshpande and Karypis 2004) is one of the most successful approaches, taking advantage of user rating history to predict users' interests. User-based CF and item-based CF are mainly involved in the CF method.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Coupled Item-Based Matrix Factorization

Cao

2014

Web Information Systems Engineering – WISE 2014

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“…Cao et al [2] proposed a correlated Hidden Markov detection model to deal with the problem that abnormal data may raise the global sensitivity. They detected and removed the abnormal data by applying the one-step transition probability, which can decrease the noise level added to the original data.…”

Section: Related Workmentioning

confidence: 99%

“…Existing differential privacy preserving methods for correlated time series can be categorized into model-based methods and transform-based methods. The model-based methods rebuilt the sensitivity function by applying correlation models, such as Markov [2] and Bayesian [3] correlation models, or by applying coefficient matrix models [4]. The transform-based methods can be divided into two types.…”

Section: Introductionmentioning

confidence: 99%

An Attack Model on Differential Privacy Preserving Methods for Correlated Time Series

Xiong¹,

Zheng-quan²,

Wang³

2017

IJDTA

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“…In fact, due to the interaction between multi-type objects guided by multi-type relations, besides objects as clustering target, it is often necessary to explicitly consider the contributions by those nontarget associated objects, and relations between target objects and non-target objects or even between non-target objects themselves. This involves the necessity of considering coupling relationships (Cao et al 2012) between heterogeneous objects and between relations and objects.…”

Section: Introductionmentioning

confidence: 99%

A novel probabilistic clustering model for heterogeneous networks

Deng

2016

Mach Learn

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Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and trade-off between integrating relevant objects and reducing the noises caused by relations across objects. In this paper, we propose a general probabilistic graphical model for clustering heterogeneous networks. First, we present a novel graphical representation based on our basic assumptions: different relation types produce different weight distributions to specify intra-cluster probability between two objects, and clusters are formed around cluster cores. Then, we derive an efficient algorithm called PROCESS, standing for PRObabilistic Clustering modEl for heterogeneouS networkS. PROCESS employs a balance-controlled message passing algorithm and mathematical programming for inference and estimation. Experimental results show that our approach is effective and significantly outperforms the state-of-the-art algorithms on both synthetic and real data from heterogeneous networks.

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Coupled Behavior Analysis with Applications

Cited by 142 publications

References 28 publications

Coupled Item-Based Matrix Factorization

Coupled Item-Based Matrix Factorization

An Attack Model on Differential Privacy Preserving Methods for Correlated Time Series

A novel probabilistic clustering model for heterogeneous networks

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