Random Walk Based Resource Allocation: Predicting and Recommending Links in Cross-Operator Mobile Communication Networks

Dong, Yuxiao; Ke, Qing; Rao, Junhui; Wu, Bin

doi:10.1109/icdmw.2011.143

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Users can download and execute Apps in their mobile devices to satisfy their needs and affinities. For App providers, to understand users' preferences is quite important to recommend new Apps, and to decide their marketing strategies for selling Apps [1][2][3]. Although users can rate the Apps they have experienced, only a small percentage of users rate their Apps.…”

Section: Introductionmentioning

confidence: 99%

Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Liao

Peng

2013

Advances in Knowledge Discovery and Data Mining

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Abstract. Due to a huge amount of mobile applications (abbreviated as Apps), for Apps providers, the usage preferences of Apps are important in recommending Apps, downloading Apps and promoting Apps. We predict and quantize users' dynamic preferences by exploring their usage traces of Apps. To address the dynamic preference prediction problem, we propose Mode-based Prediction (abbreviated as MBP) and Referencebased Prediction (abbreviated as RBP) algorithms. Both MBP and RBP consist of two phases: the trend detection phase and the change estimation phase. In the trend detection phase, both algorithms determine whether the preference of an App is increasing or decreasing. Then, in the change estimation phase, the amount of preference change is calculated. In particular, MBP adopts users' current usage mode (active or inactive), and then estimates the amount of change via our proposed utility model. On the other hand, RBP calculates an expected number of usage as a reference, and then builds a probabilistic model to estimate the change of preference by comparing the real usage and the reference. We conduct comprehensive experiments using two App usage traces and one music listening log, the Last.fm dataset, to validate our proposed algorithms. The experimental results show that both MBP and RBP outperform the usage-based method that is based solely on the number of usages.

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Section: Introductionmentioning

confidence: 99%

Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Liao

Peng

2013

Advances in Knowledge Discovery and Data Mining

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“…The prediction of relationships within a social network is a task that has gained attention in recent years, because it may helps from finding friends who were not connected in an online social network [Vasuki et al 2010, Tian et al 2010, Perez et al 2012, Fire et al 2011, Zhong et al 2013, Quercia and Capra 2009, to enhance the performance of work in companies or in universities [Hsieh et al 2013, Dong et al 2012, de Sa and Prudencio 2011. In the academic social network context, the link prediction is mainly used for predicting coauthorships, whose purpose is to indicate a pair of researchers that potentially will collaborate in the publication of papers [Guo and Guo 2010, Makrehchi 2011, Dong et al 2011, Gao et al 2012, Lin et al 2012, Digiampietri et al 2013, Digiampietri et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Co-authorship prediction in academic social network

Maruyama

Digiampietri

2016

Anais Do v Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2016)

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The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

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Random Walk Based Resource Allocation: Predicting and Recommending Links in Cross-Operator Mobile Communication Networks

Cited by 2 publications

References 30 publications

Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Co-authorship prediction in academic social network

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