Forecasting Popularity of Videos Using Social Media

Xu, Jie; Schaar, Mihaela van der; Liu, Jiangchuan; Li, Haitao

doi:10.1109/jstsp.2014.2370942

Cited by 53 publications

(33 citation statements)

References 37 publications

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“…Unfortunately, selecting and fitting these models to data is a time-demanding task [11], and thus not suitable for fast-changing environments. There are also several modelbased/free approaches for predicting content popularity using statistical analysis, transfer learning, or social network properties, see [17]- [20]. Yet, these works do not incorporate the predictions into the system operation.…”

Section: A Reactive Policiesmentioning

confidence: 99%

Online Convex Optimization for Caching Networks

Paschos¹,

Destounis

Iosifidis

2020

IEEE/ACM Trans. Networking

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We study the problem of wireless edge caching when file popularity is unknown and possibly non-stationary. A bank of J caches receives file requests and a utility is accrued for each request depending on the serving cache. The network decides dynamically which files to store at each cache and how to route them, in order to maximize total utility. The request sequence is assumed to be drawn from an arbitrary distribution, capturing time-variance, temporal and spatial locality of requests. For this challenging setting, we propose the Bipartite Supergradient Caching Algorithm (BSCA) which provably exhibits no regret (RT /T → 0). That is, as the time horizon T increases, BSCA achieves (at least) the same utility with the cache configuration that we would have chosen knowing all future requests. The learning rate of the algorithm is characterized by its regret expression RT = O( √ JT ), which is independent of the file library size. For the single-cache case, we prove that this is the lowest attainable bound. BSCA requires at each step J projections on intersections of boxes and simplices, for which we propose a tailored algorithm. Our model is the first that draws a connection between the network caching problem and Online Convex Optimization, and we demonstrate its generality by discussing various practical extensions and presenting a tracedriven comparison with state-of-the-art competitors.

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Section: A Reactive Policiesmentioning

confidence: 99%

Online Convex Optimization for Caching Networks

Paschos¹,

Destounis

Iosifidis

2020

IEEE/ACM Trans. Networking

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“…This work finds that videos exhibit a geographical distribution of interest, with users arising from a confined and single area rather than from a global area, and it provides new insights on how the geographic reach of a video changes as its popularity peak and then fades away. The prediction of video popularity has also been studied based on historical information given by early popularity measures [10], [15]. Two novel models are proposed, which are able to better distinguish between videos with different popularities, by assigning different weights to samples with different popularities and exploring the similarity between the video and known samples within the monitoring period.…”

Section: A Video Measurementmentioning

confidence: 99%

“…where d (t−W,t−1) li is the fraction of request number of user i over total request number in location l, f (t−W,t−1) ij is the request distribution of user i in location j, and U l is the set 10 Fig. 19: Performance comparison (the percentage number in brackets is the ratio of cache capacity to the total number of content).…”

Section: A Caching Strategymentioning

confidence: 99%

“…However, such measurement studies are challenging because many different factors are involved, including user behaviors (i.e., mobility pattern and video preference), video content characteristics, and mobile network characteristics. Previous studies generally focus on a single aspect, e.g., studying the popularity of mobile video content [9], [10], user mobility behaviors [11], or network strategies to support mobile video streaming, e.g., content replication [12]. The limitation of the previous studies is that they have not considered the joint impact of user behaviors, content characteristics and wireless network deployment, on edge network content delivery.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Performance of Edge Content Caching for Mobile Video Streaming

Wang

Zhang

et al. 2017

IEEE J. Select. Areas Commun.

153

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Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks-from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)-to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. We reveal that content, location and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU. Index Terms-Edge network, mobile video streaming, user behavior, measurement, content delivery.

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“…Researchers analysed several types of online content, including news articles [13], Twitter messages [17], [18], images [15], [19] and videos [12], [14], [20], [21]. Proposed prediction methods rely either on intrinsic features of the content, such as visual or textual cues [13], [15], [19], or on social features describing the arXiv:1510.06223v4 [cs.SI] 12 May 2017 structure of the social network [16] or on early distribution patterns [11], [14]. To our knowledge, not too much attention was paid to the problem of combining different cues to predict the popularity of the online content in the context of videos.…”

Section: Introductionmentioning

confidence: 99%

Predicting Popularity of Online Videos Using Support Vector Regression

Trzciński

Rokita

2017

IEEE Trans. Multimedia

164

106

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Abstract-In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector Regression with Gaussian Radial Basis Functions. We show that predicting popularity patterns with this approach provides more precise and more stable prediction results, mainly thanks to the non-linear character of the proposed method as well as its robustness. We prove the superiority of our method against the state of the art using datasets containing almost 24,000 videos from YouTube and Facebook. We also show that using visual features, such as the outputs of deep neural networks or scene dynamics' metrics, can be useful for popularity prediction before content publication. Furthermore, we show that popularity prediction accuracy can be improved by combining early distribution patterns with social and visual features and that social features represent a much stronger signal in terms of video popularity prediction than the visual ones.

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Forecasting Popularity of Videos Using Social Media

Cited by 53 publications

References 37 publications

Online Convex Optimization for Caching Networks

Online Convex Optimization for Caching Networks

Understanding Performance of Edge Content Caching for Mobile Video Streaming

Predicting Popularity of Online Videos Using Support Vector Regression

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