Nowadays, Cable TV operators provide their users multiple ways to watch TV content, such as Live TV and Video on Demand (VOD) services. In the last years, Catch-up TV has been introduced, allowing users to watch recent broadcast content whenever they want to. Understanding how the users interact with such services is important to develop solutions that may increase user satisfaction , user engagement and user consumption. In this paper, we characterize, for the first time, how users interact with a large European Cable TV operator that provides Live TV, Catch-up TV and VOD services. We analyzed many characteristics, such as the service usage, user engagement, program type, program genres and time periods. This characterization will help us to have a deeper understanding on how users interact with these different services, that may be used to enhance the recommendation systems of Cable TV providers.
Some authors performed dataset characterizations that helped in the design of their recommendation systems. @cite_8 presented a high-level dataset characterization and showed that on average more video content is consumed in the weekend. The average number of views for a video decreases exponentially as the time passes. The authors attribute this result to the users viewing behavior and the user interface of the Catch-up TV portal.
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Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant. IPTV services deployed nowadays often consist of both live TV and Video-on-Demand (VoD), offered by the same service provider to the same pool of users over the same managed network. Understanding user behaviors in such a setting is hence an important step for system modelling and optimization. Previous studies on user behavior on video services were on either live TV or VoD. For the first time, we conduct an in-depth large-scale behavior study for IPTV users offering simultaneously live TV and VoD choices at the same time. Our data is from the largest IPTV service provider in China, offering hundreds of live channels and hundreds of thousands of VoD files, with traces covering more than 1.9 million users over a period of 5 months. This large dataset provides us a unique opportunity to cross-compare user viewing behaviors for these services on the same platform, and sheds valuable insights on how users interact with such a simultaneous system. Our results lead to new understanding on IPTV user behaviors which have strong implications on system design. For example, we find that the average holding time for VoD is significantly longer than live TV. live TV users tend to surf more. However, if such channel surfing is discounted, the holding times of both services are not much different. While users in VoD tend to view HD longer, channel popularity for live TV is much less dependent on its video quality. In contrast to some popular assumptions on user interactivity, the transitions among live TV, VoD, and offline modes are far from a Markov model. As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events. We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1 increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content. Predicting Web content popularity is an important task for supporting the design and evaluation of a wide range of systems, from targeted advertising to effective search and recommendation services. We here present two simple models for predicting the future popularity of Web content based on historical information given by early popularity measures. Our approach is validated on datasets consisting of videos from the widely used YouTube video-sharing portal. Our experimental results show that, compared to a state-of-the-art baseline model, our proposed models lead to significant decreases in relative squared errors, reaching up to 20 reduction on average, and larger reductions (of up to 71 ) for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity. ABSTRACT Social networks allow people to connect with each otherand have conversations on a wide variety of topics. How-ever, users tend to connect with like-minded people and readagreeable information, a behavior that leads to group polar-ization. Motivated by this scenario, we study how to takeadvantage of partial homophily to suggest agreeable contentto users authored by people with opposite views on sensitiveissues. We introduce a paradigm to present a data portraitof users, in which their characterizing topics are visualizedand their corresponding tweets are displayed using an or-ganic design. Among their tweets we inject recommendedtweets from other people considering their views on sensitiveissues in addition to topical relevance, indirectly motivatingconnections between dissimilar people. To evaluate our ap-proach, we present a case study on Twitter about a sensitivetopic in Chile, where we estimate user stances for regularpeople and nd intermediary topics. We then evaluated ourdesign in a user study. We found that recommending topi-cally relevant content from authors with opposite views in abaseline interface had a negative emotional e ect. We sawthat our organic visualization design reverts that e ect. Wealso observed signi cant individual di erences linked to eval-uation of recommendations. Our results suggest that organicvisualization may revert the negative e ects of providingpotentially sensitive content. Online social networks pose an interesting problem: how to best characterize the different classes of user behavior. Traditionally, user behavior characterization methods, based on user individual features, are not appropriate for online networking sites. In these environments, users interact with the site and with other users through a series of multiple interfaces that let them to upload and view content, choose friends, rank favorite content, subscribe to users and do many other interactions. Different interaction patterns can be observed for different groups of users. In this paper, we propose a methodology for characterizing and identifying user behaviors in online social networks. First, we crawled data from YouTube and used a clustering algorithm to group users that share similar behavioral pattern. Next, we have shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors. Finally, we present and discuss experimental results of the use of proposed methodology. A set of useful profiles, derived from the analysis of the YouTube sample is presented. The identification of different classes of user behavior has the potential to improve, for instance, recommendation systems for advertisements in online social networks. Although video recommender systems have become the predominant way for people to obtain video information, their performances are far from satisfactory in that (1) the recommended videos are often mismatched with the users' interests and (2) the recommendation results are, in most cases, hardly understandable for users and therefore cannot persuade them to engage. In this paper, we attempt to address the above problems in data representation level, and aim to learn a common attributed representation for users and videos in social media with good interpretability, stability and an appropriate level of granularity. The basic idea is to represent videos with users' social attributes, and represent users with content attributes of videos, such that both users and videos can be represented in a common space concatenated by social attributes and content attributes. The video recommendation problem can then be converted into a similarity matching problem in the common space. However, it is still challenging to balance the roles of social attributes and content attributes, learn such a common representation in sparse user-video interactions and deal with the cold-start problem. In this paper, we propose a REgularized Dual-fActor Regression (REDAR) method based on matrix factorization. In this method, social attributes and content attributes are flexibly combined, and social and content information are effectively exploited to alleviate the sparsity problem. An incremental version of REDAR is designed to solve the cold-start problem. We extensively evaluate the proposed method for video recommendation application in real social network dataset, and the results show that, in most cases, the proposed method can achieve a relative improvement of more than 20 compared to state-of-the-art baseline methods. Understanding content popularity growth is of great importance to Internet service providers, content creators and online marketers. In this work, we characterize the growth patterns of video popularity on the currently most popular video sharing application, namely YouTube. Using newly provided data by the application, we analyze how the popularity of individual videos evolves since the video's upload time. Moreover, addressing a key aspect that has been mostly overlooked by previous work, we characterize the types of the referrers that most often attracted users to each video, aiming at shedding some light into the mechanisms (e.g., searching or external linking) that often drive users towards a video, and thus contribute to popularity growth. Our analyses are performed separately for three video datasets, namely, videos that appear in the YouTube top lists, videos removed from the system due to copyright violation, and videos selected according to random queries submitted to YouTube's search engine. Our results show that popularity growth patterns depend on the video dataset. In particular, copyright protected videos tend to get most of their views much earlier in their lifetimes, often exhibiting a popularity growth characterized by a viral epidemic-like propagation process. In contrast, videos in the top lists tend to experience sudden significant bursts of popularity. We also show that not only search but also other YouTube internal mechanisms play important roles to attract users to videos in all three datasets. In this paper, we examine mobile users' behavior and their corresponding video viewing patterns from logs extracted from the servers of a large scale VoD system. We focus on the analysis of the main discrepancies that might exist when users access the VoD system catalog from WiFi or 3G connections. We also study factors that might impact mobile users' interests and video popularity. The users' behavior exhibits strong daily and weekly patterns, with mobile users' interests being surprisingly spread across almost all categories and video lengths, independently of the connection type. However, by examining the activity of users individually, we observed a concentration of interests and peculiar access patterns, which allows to classify the users and thus better predict their behavior. We also find a skewed video popularity distribution and then demonstrate that the popularity of a video can be predicted using its very early popularity level. We further analyzed the sources of video viewing and found that even if search engines are the dominant sources for a majority of videos, they represent less than 10 (resp. 20 ) of the sources for the highly popular videos in 3G (resp. WiFi) network. We report that both the type of connections and mobile devices in use have an impact on the viewing time and the source of viewing. Based on our findings, we provide insights and recommendations that can be used to design intelligent mobile VoD systems and help improving personalized services on these platforms. The traffic of multi-view video is several times larger than traditional multimedia, which brings much more increment in the bandwidth requirement. To overcome this problem, Simulcast and MVC (Multi-view Video Coding) can be used to decrease the traffic for multi-view video. However, although Simulcast removes redundant information within each view, it still contains a large amount of inter-view redundant information. MVC further removes the correlation between the views, but all the views should be encoded and transmitted. So the bit-rates are still too high in these schemes. In this paper, in order to reduce the bit-rate for multi-view video transmission, we analyze the user's motion which is classified into random access model and successive motion model. We find that in the successive motion model only part of frames are possible to be displayed in a period of time. According to the analytical result, we propose a user dependent scheme to decrease the bit-rate of multi-view video transmission. The proposed scheme only encodes and transmits those frames that are possible to be displayed according to the user's motion. Further, in order to support the proposed scheme, we also propose a prediction structure as a substitute of the prediction structure in MVC. Performance evaluation proves that this proposed scheme reduces the average bit-rate for the transmission of multi-view video. We present a method for accurately predicting the long time popularity of online content from early measurements of user's access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. Although according to surveys related to internet user activity it is considered one of the most popular aspects, few studies are actually concerned with internet pornography. This paper is aimed at rectifying that overlook. In particular, we study user activity related to internet pornography by looking at two main behaviors: (i) watching pornography, and (ii) providing feedback on pornography items in the form of ratings and comments. By using appropriate datasets that we collect, we make contributions related to the study of both behaviors pointed out above. With regards to viewing, we observe that views are highly dependent on pornography category and video size. By studying the feedback system of pornography video websites, we observe differences in the way users rate items across websites popular in different parts of the world. Finally, we employ sentiment analysis to study the comments that users leave on pornography websites and we find surprising similarities across the analyzed websites. Our results pave the way to understanding more about human behavior related to internet pornography and can impact, among others, fields such as content personalization, video content delivery, recommender systems This technical demo presents Emoji2Video, a query-by-emoji interface for exploring video collections. Ideogram-based video search and representation presents an opportunity for an intuitive, visual interface and concise non-textual summary of video contents, in a form factor that is ideal for small screens. The demo allows users to build search strings comprised of ideograms which are used to query a large dataset of YouTube videos. The system returns a list of the top-ranking videos for the user query along with an emoji summary of the video contents so that users may make an informed decision whether to view a video or refine their search terms. The ranking of the videos is done in a zero-shot, multi-modal manner that employs an embedding space to exploit semantic relationships between user-selected ideograms and the video's visual and textual content. Video production is a collaborative process in which stakeholders regularly review drafts of the edited video to indicate problems and offer suggestions for improvement. Although practitioners prefer in-person feedback, most reviews are conducted asynchronously via email due to scheduling and location constraints. The use of this impoverished medium is challenging for both providers and consumers of feedback. We introduce VidCrit, a system for providing asynchronous feedback on drafts of edited video that incorporates favorable qualities of an in-person review. This system consists of two separate interfaces: (1) A feedback recording interface captures reviewers' spoken comments, mouse interactions, hand gestures and other physical reactions. (2) A feedback viewing interface transcribes and segments the recorded review into topical comments so that the video author can browse the review by either text or timelines. Our system features novel methods to automatically segment a long review session into topical text comments, and to label such comments with additional contextual information. We interviewed practitioners to inform a set of design guidelines for giving and receiving feedback, and based our system's design on these guidelines. Video reviewers using our system preferred our feedback recording interface over email for providing feedback due to the reduction in time and effort. In a fixed amount of time, reviewers provided 10.9 (σ=5.09) more local comments than when using text. All video authors rated our feedback viewing interface preferable to receiving feedback via e-mail. We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows. A number of online video social networks, out of which YouTube is the most popular, provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users--promoters--may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we go a step further by addressing the issue of detecting video spammers and promoters. Towards that end, we manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimates. Using our test collection, we provide a characterization of social and content attributes that may help distinguish each user class. We also investigate the feasibility of using a state-of-the-art supervised classification algorithm to detect spammers and promoters, and assess its effectiveness in our test collection. We found that our approach is able to correctly identify the majority of the promoters, misclassifying only a small percentage of legitimate users. In contrast, although we are able to detect a significant fraction of spammers, they showed to be much harder to distinguish from legitimate users. A number of online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, content promoters may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we first manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimate users. Using our test collection, we provide a characterization of content, individual, and social attributes that help distinguish each user class. We then investigate the feasibility of using supervised classification algorithms to automatically detect spammers and promoters, and assess their effectiveness in our test collection. While our classification approach succeeds at separating spammers and promoters from legitimate users, the high cost of manually labeling vast amounts of examples compromises its full potential in realistic scenarios. For this reason, we further propose an active learning approach that automatically chooses a set of examples to label, which is likely to provide the highest amount of information, drastically reducing the amount of required training data while maintaining comparable classification effectiveness. The distribution of videos over the Internet is drastically transforming how media is consumed and monetized. Content providers, such as media outlets and video subscription services, would like to ensure that their videos do not fail, startup quickly, and play without interruptions. In return for their investment in video stream quality, content providers expect less viewer abandonment, more viewer engagement, and a greater fraction of repeat viewers, resulting in greater revenues. The key question for a content provider or a CDN is whether and to what extent changes in video quality can cause changes in viewer behavior. Our work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies. To establish causality, we use Quasi-Experimental Designs, a novel technique adapted from the medical and social sciences. We study the impact of video stream quality on viewer behavior in a scientific data-driven manner by using extensive traces from Akamai's streaming network that include 23 million views from 6.7 million unique viewers. We show that viewers start to abandon a video if it takes more than 2 seconds to start up, with each incremental delay of 1 second resulting in a 5.8 increase in the abandonment rate. Further, we show that a moderate amount of interruptions can decrease the average play time of a viewer by a significant amount. A viewer who experiences a rebuffer delay equal to 1 of the video duration plays 5 less of the video in comparison to a similar viewer who experienced no rebuffering. Finally, we show that a viewer who experienced failure is 2.32 less likely to revisit the same site within a week than a similar viewer who did not experience a failure. User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications. The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems.