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.
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.
@cite_31 @cite_27 @cite_18 @cite_15
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Time information plays a crucial role on social media popularity. Existing research on popularity prediction, effective though, ignores temporal information which is highly related to user-item associations and thus often results in limited success. An essential way is to consider all these factors (user, item, and time), which capture the dynamic nature of photo popularity. In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for exploring the mechanism of dynamic popularity. The user-item context provides a holistic view of popularity, while the time-sensitive context captures the temporal dynamics nature of popularity. Accordingly, we develop two kinds of time-sensitive features, including user activeness variability and photo prevalence variability. To predict photo popularity, we propose a novel framework named Multi-scale Temporal Decomposition (MTD), which decomposes the popularity matrix in latent spaces based on contextual associations. Specifically, the proposed MTD models time-sensitive context on different time scales, which is beneficial to automatically learn temporal patterns. Based on the experiments conducted on a real-world dataset with 1.29M photos from Flickr, our proposed MTD can achieve the prediction accuracy of 79.8 and outperform the best three state-of-the-art methods with a relative improvement of 9.6 on average. Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes. 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 . The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9 -47.5 in terms of prediction accuracy. It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23 and the total duration improved by 10 , compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month. Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters. We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional trait space' in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences. Here we present three alternatives: direct observation of an absolute rating each user gives to some items, observation of a binary preference (like don't like) and observation of a set of ordinal ratings on a user-specific scale. Efficient inference is achieved by approximate message passing involving a combination of Expectation Propagation (EP) and Variational Message Passing. We also include a dynamics model which allows an item's popularity, a user's taste or a user's personal rating scale to drift over time. By using Assumed-Density Filtering (ADF) for training, the model requires only a single pass through the training data. This is an on-line learning algorithm capable of incrementally taking account of new data so the system can immediately reflect the latest user preferences. We evaluate the performance of the algorithm on the MovieLens and Netflix data sets consisting of approximately 1,000,000 and 100,000,000 ratings respectively. This demonstrates that training the model using the on-line ADF approach yields state-of-the-art performance with the option of improving performance further if computational resources are available by performing multiple EP passes over the training data. The success of query-by-concept, proposed recently to cater to video retrieval needs, depends greatly on the accuracy of concept-based video indexing. Unfortunately, it remains a challenge to recognize the presence of concepts in a video segment or to extract an objective linguistic description from it because of the semantic gap, that is, the lack of correspondence between machine-extracted low-level features and human high-level conceptual interpretation. This paper studies three issues with the aim to reduce such a gap: 1) how to explore cues beyond low-level features, 2) how to combine diverse cues to improve performance, and 3) how to utilize the learned knowledge when applying it to a new domain. To solve these problems, we propose a framework that jointly exploits multiple cues across multiple video domains. First, recursive algorithms are proposed to learn both interconcept and intershot relationships from annotations. Second, all concept labels for all shots are simultaneously refined in a single fusion model. Additionally, unseen shots are assigned pseudolabels according to their initial prediction scores so that contextual and temporal relationships can be learned, thus requiring no additional human effort. Integration of cues embedded within training and testing video sets accommodates domain change. Experiments on popular benchmarks show that our framework is effective, achieving significant improvements over popular baselines. Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49 enhancement on existing users and 115 enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain. As the rise of the portable devices, people usually access the social media such as Twitter and Facebook through wireless networks. Therefore, data transmission rates significant important to the end users. In this work, we discuss the problem of context-aware data caching in the heterogeneous small cell networks to reduce the service delay and how the device-to-device (D2D) and device-to-infrastructure (D2I) improve the system social welfare. In the data-caching model, we explore three types of cache entities, macro cell base stations, small cell base stations, and end user devices. We propose a long short-term memory (LSTM) deep learning model to perform data analysis and extract information content from the data. By knowing the interest of the data to the cache entities, we can cache the data that will most likely to be requested by the end users to reduce service latency. In simulation, we show our proposed algorithm can efficiently reduce the service latency during 2016 U.S. presidential election where mobile user were urgent to request the election information through wireless networks. Comparing with other mechanisms such as using one-to-many matching algorithm or without D2D communication technology, our proposed algorithm improves significantly on the devices performance and system social welfare. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. In this paper, we consider the problem of preserving privacy in the context of online learning. Online learning involves learning from data in real-time, due to which the learned model as well as its predictions are continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by observing changes in the subsequent predictions. Furthermore, with more and more online systems (e.g. search engines like Bing, Google etc.) trying to learn their customers’ behavior by leveraging their access to sensitive customer data (through cookies etc.), the problem of privacy preserving online learning has become critical. We study the problem in the framework of online convex programming (OCP)—a popular online learning setting with several theoretical and practical implications—while using differential privacy as the formal measure of privacy. For this problem, we provide a generic framework that can be used to convert any given OCP algorithm into a private OCP algorithm with provable privacy as well as regret guarantees (utility), provided that the given OCP algorithm satisfies the following two criteria: 1) linearly decreasing sensitivity, i.e., the effect of the new data points on the learned model decreases linearly, 2) sub-linear regret. We then illustrate our approach by converting two popular OCP algorithms into corresponding differentially private algorithms while guaranteeing O( p T ) regret for strongly convex functions. Next, we consider the practically important class of online linear regression problems, for which we generalize the approach by (2010a) to provide a differentially private algorithm with just poly-log regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for the offline learning problem as well. For the offline learning problem, our approach guarantees better error bounds and is more practical than the existing state-of-the-art methods (, 2011; , 2009). We investigate the problem of optimal request routing and content caching in a heterogeneous network supporting in-network content caching with the goal of minimizing average content access delay. Here, content can either be accessed directly from a back-end server (where content resides permanently) or be obtained from one of multiple in-network caches. To access a piece of content, a user must decide whether to route its request to a cache or to the back-end server. Additionally, caches must decide which content to cache. We investigate the problem complexity of two problem formulations, where the direct path to the back-end server is modeled as i) a congestion-sensitive or ii) a congestion-insensitive path, reflecting whether or not the delay of the uncached path to the back-end server depends on the user request load, respectively. We show that the problem is NP-complete in both cases. We prove that under the congestion-insensitive model the problem can be solved optimally in polynomial time if each piece of content is requested by only one user, or when there are at most two caches in the network. We also identify a structural property of the user-cache graph that potentially makes the problem NP-complete. For the congestion-sensitive model, we prove that the problem remains NP-complete even if there is only one cache in the network and each content is requested by only one user. We show that approximate solutions can be found for both models within a (1-1 e) factor of the optimal solution, and demonstrate a greedy algorithm that is found to be within 1 of optimal for small problem sizes. Through trace-driven simulations we evaluate the performance of our greedy algorithms, which show up to a 50 reduction in average delay over solutions based on LRU content caching. The frequently changing user preferences and or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach. We consider a basic content distribution scenario consisting of a single origin server connected through a shared bottleneck link to a number of users each equipped with a cache of finite memory. The users issue a sequence of content requests from a set of popular files, and the goal is to operate the caches as well as the server such that these requests are satisfied with the minimum number of bits sent over the shared link. Assuming a basic Markov model for renewing the set of popular files, we characterize approximately the optimal long-term average rate of the shared link. We further prove that the optimal online scheme has approximately the same performance as the optimal offline scheme, in which the cache contents can be updated based on the entire set of popular files before each new request. To support these theoretical results, we propose an online coded caching scheme termed coded least-recently sent (LRS) and simulate it for a demand time series derived from the dataset made available by Netflix for the Netflix Prize. For this time series, we show that the proposed coded LRS algorithm significantly outperforms the popular least-recently used (LRU) caching algorithm. In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets --- crawled from YouTube, Flickr and Last.fm --- show that our method consistently outperforms competitive baselines in several evaluation tasks. 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. Video popularity prediction plays a foundational role in many aspects of life, such as recommendation systems and investment consulting. Because of its technological and economic importance, this problem has been extensively studied for years. However, four constraints have limited most related works' usability. First, most feature oriented models are inadequate in the social media environment, because many videos are published with no specific content features, such as a strong cast or a famous script. Second, many studies assume that there is a linear correlation existing between view counts from early and later days, but this is not the case in every scenario. Third, numerous works just take view counts into consideration, but discount associated sentiments. Nevertheless, it is the public opinions that directly drive a video's final success failure. Also, many related approaches rely on a network topology, but such topologies are unavailable in many situations. Here, we propose a Dual Sentimental Hawkes Process (DSHP) to cope with all the problems above. DSHP's innovations are reflected in three ways: (1) it breaks the "Linear Correlation" assumption, and implements Hawkes Process; (2) it reveals deeper factors that affect a video's popularity; and (3) it is topology free. We evaluate DSHP on four types of videos: Movies, TV Episodes, Music Videos, and Online News, and compare its performance against 6 widely used models, including Translation Model, Multiple Linear Regression, KNN Regression, ARMA, Reinforced Poisson Process, and Univariate Hawkes Process. Our model outperforms all of the others, which indicates a promising application prospect. A heterogenous network with base stations (BSs), small base stations (SBSs), and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an @math difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as @math , where @math is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of @math and scales linearly with the dimension of the distribution parameter. We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting pose background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two sub-networks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).