text: Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an @math filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.
  @cite_31: Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations, since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user’s context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14 .
  @cite_27: Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges.
  @cite_18: Previous research on online media popularity prediction concluded that the rise in popularity of online videos maintains a conventional logarithmic distribution. However, recent studies have shown that a significant portion of online videos exhibit bursty sudden rise in popularity, which cannot be accounted for by video domain features alone. In this paper, we propose a novel transfer learning framework that utilizes knowledge from social streams (e.g., Twitter) to grasp sudden popularity bursts in online content. We develop a transfer learning algorithm that can learn topics from social streams allowing us to model the social prominence of video content and improve popularity predictions in the video domain. Our transfer learning framework has the ability to scale with incoming stream of tweets, harnessing physical world event information in real-time. Using data comprising of 10.2 million tweets and 3.5 million YouTube videos, we show that social prominence of the video topic (context) is responsible for the sudden rise in its popularity where social trends have a ripple effect as they spread from the Twitter domain to the video domain. We envision that our cross-domain popularity prediction model will be substantially useful for various media applications that could not be previously solved by traditional multimedia techniques alone.
  @cite_15: This paper presents Trend-Caching, a novel cache replacement method that optimizes cache performance according to the trends of video content. Trend-Caching explicitly learns the popularity trend of video content and uses it to determine which video it should store and which it should evict from the cache. Popularity is learned in an online fashion and requires no training phase, hence it is more responsive to continuously changing trends of videos. We prove that the learning regret of Trend-Caching (i.e., the gap between the hit rate achieved by Trend-Caching and that by the optimal caching policy with hindsight) is sublinear in the number of video requests, thereby guaranteeing both fast convergence and asymptotically optimal cache hit rate. We further validate the effectiveness of Trend-Caching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40 when the cache capacity is limited. Furthermore, Trend-Caching has low complexity.
summary: Unfortunately, often times content popularity profile can not only be unknown , but also time-varying. This is because user's interests change constantly, and meanwhile new contents are being created @cite_27 . As a result, learning-based caching algorithms should be designed in an online fashion, i.e., requiring no training phase, and adaptive to popularity fluctuations. To this end, Roy proposed to predict video popularity by utilizing knowledge from the social streams @cite_18 . M "uller introduced context-aware proactive caching @cite_31 . By constructing context space based on user information, they proposed an online algorithm that first learns context-specific user demands, and then updates cached contents accordingly. Other information has also been used for context differentiation, such as content features and system states @cite_15 . The prediction accuracy of those solutions is highly dependent on the information used for context differentiation. To content service providers, however, user information is extremely sensitive and often unavailable. In addition, it is also impossible for them to get detailed system or network information when making caching decisions.