Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

Figueiredo, Flávio; Almeida, Jussara M.; Matsubara, Yasuko; Ribeiro, Bruno; Faloutsos, Christos

doi:10.1007/978-3-662-44848-9_25

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…Although TribeFlow is able to predict not just the next-item but also the next n ≥ 1 items, our evaluations are based at next-item predictions because previous efforts mostly focused their evaluations on this task. We also consider the reconsumption problem (consecutive visits to the same item) treated in Figueiredo et al [14] as a separate, often easier, problem that can be dealt with via stochastic complementation as discussed in Section 3.1.…”

Section: Resultsmentioning

confidence: 99%

TribeFlow

Figueiredo

Ribeiro

Almeida

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). But what users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413× faster than top competitors.

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Section: Resultsmentioning

confidence: 99%

TribeFlow

Figueiredo

Ribeiro

Almeida

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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“…The work in [13] investigated the effect of revisits on content popularity, while [56] focused on the daily number of active users. Prakash et al [52] described a case where two competing products/ideas spreading over the network, and provided a theoretical analysis of the propagation model (winner takes all: WTA) for arbitrary graph topology.…”

Section: Spikes and Propagationmentioning

confidence: 99%

Ecosystem on the Web: non-linear mining and forecasting of co-evolving online activities

2016

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Given a large collection of co-evolving online activities, such as searches for the keywords "Xbox", "PlayStation" and "Wii", how can we find patterns and rules? Are these keywords related? If so, are they competing against each other? Can we forecast the volume of user activity for the coming month? We conjecture that online activities compete for user attention in the same way that species in an ecosystem compete for food. We present ECOWEB, (i.e., Ecosystem on the Web), which is an intuitive model designed as a non-linear dynamical system for mining large-scale co-evolving online activities. Our second contribution is a novel, parameter-free, and scalable fitting algorithm, ECOWEB-FIT, that estimates the parameters of ECOWEB. Extensive experiments on real data show that ECOWEB is effective, in that it can capture long-range dynamics and meaningful patterns such as seasonalities, and practical, in that it can provide accurate long-range forecasts. ECOWEB consistently outperforms existing methods in terms of both accuracy and execution speed.

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“…Analyses of epidemics, blogs, social media, propagation and the cascades they create have attracted much interest. We answer several important topics such as how popularity of "memes" changes over time [21]; how to find temporal patterns in information diffusion process through online media, e.g., blogs, hashtags [54,53], and YouTube [8,11]; how to describe rising and falling patterns of information propagation (e.g., memes, hashtags and keyword search volume) using non-linear dynamical systems [31,29].…”

Section: Linear Modeling and Summarizationmentioning

confidence: 99%

Mining and Forecasting of Big Time-series Data

Sakurai¹

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find patterns in large-scale time-series sequences. We review the state of the art in four related fields: (1) similarity search and pattern discovery, (2) linear modeling and summarization, (3) non-linear modeling and forecasting, and (4) the extension of time-series mining and tensor analysis. The emphasis of the tutorial is to provide the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to introduce case studies that illustrate their practical use.

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Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

Cited by 13 publications

References 20 publications

TribeFlow

TribeFlow

Ecosystem on the Web: non-linear mining and forecasting of co-evolving online activities

Mining and Forecasting of Big Time-series Data

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