Cited By
View all- Liao XZhang LWei JYang DChen G(2018)Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster ApproachWireless Communications & Mobile Computing10.1155/2018/94342392018Online publication date: 1-Jan-2018
Non-linear Time-series Analysis of Social Influence
Pages 12 - 16
Abstract
In this paper, we present Δ-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. Δ-SPOT can forecast long-range future dynamics of the keywords/queries. We use the Google Search, Twitter and MemeTracker data set for extensive experiments, which show that our method outperforms other non-linear mining methods. We also provide an online algorithm contributing to the need of monitoring multiple co-evolving data sequences.
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Index Terms
- Non-linear Time-series Analysis of Social Influence
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Published: 14 June 2016
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SIGMOD/PODS'16
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SIGMOD/PODS'16: International Conference on Management of Data
June 26, 2016
California, San Francisco, USA
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SIGMOD'16 PhD Paper Acceptance Rate 9 of 10 submissions, 90%;
Overall Acceptance Rate 40 of 60 submissions, 67%
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View all- Liao XZhang LWei JYang DChen G(2018)Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster ApproachWireless Communications & Mobile Computing10.1155/2018/94342392018Online publication date: 1-Jan-2018
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