Predicting User Quitting Ratio in Adaptive Bitrate Video Streaming

Lebreton, Pierre; Yamagishi, Kazuhisa

doi:10.1109/tmm.2020.3044452

Cited by 9 publications

(27 citation statements)

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“…Assessing the perceived low-level quality of images [35]- [38] and videos [39]- [42] has been an important topic in multimedia. These works tried to predict the perceived quality when the quality of the multimedia content is somewhat degraded by low-level factors such as compression and noise.…”

Section: Prediction Of Subjective Scores Of Multimedia Contentmentioning

confidence: 99%

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Narahara

Yamasaki

2023

IEEE Trans. Multimedia

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This paper presents a new user experience for online apartment search using functionality and comfort as query items. Specifically, it has three technical contributions. First, we present a new dataset on the perceived functionality and comfort scores of residential floor plans using nine question statements about the level of comfort, openness, privacy, etc. Second, we propose an algorithm to predict the scores from the floor plan images. Lastly, we implement a new apartment search system and conduct a large-scale usability study using crowdsourcing. The experimental results show that our apartment search system can provide a better user experience. To the best of our knowledge, this is the first work to propose a highly accurate machine learning model for predicting the subjective functionality and comfort of apartments.

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Section: Prediction Of Subjective Scores Of Multimedia Contentmentioning

confidence: 99%

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Narahara

Yamasaki

2023

IEEE Trans. Multimedia

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“…Although previous work has defined methods to identify the bitrate ladder on the basis of quality-related features, the relationship between quality and users desire to use the services is not obvious. Therefore, in this work, a bitrate ladder estimation method is proposed that is based on the likelihood of user quitting [2].…”

Section: Contributionsmentioning

confidence: 99%

“…Finally, the last contribution of the paper lies in its evaluation as the performance of the selected bitrate ladder are evaluated using quality and quitting estimation models that account for both coding degradation stalling events in a joined manner [2], [3].…”

Section: Contributionsmentioning

confidence: 99%

Quitting Ratio-Based Bitrate Ladder Selection Mechanism for Adaptive Bitrate Video Streaming

Lebreton

Yamagishi

2023

IEEE Trans. Multimedia

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To improve users' experience and decrease their likelihood of quitting watching videos, this paper addresses the question of how to encode the videos used in adaptive bitrate (ABR) video streaming. When addressing ABR video streaming, a lot of effort has been put into developing ABR control schemes. However, ways to appropriately encode videos also need to be defined. Unlike previous approaches that focus on coding quality, this paper considers the user quitting ratio. The user quitting ratio is the percentage of users still watching videos at a given time and enables us to address the consequences of quality and stimulus duration on the decision of a user to quit. Considering the value of the user quitting ratio, this paper describes a method that uses content analysis, as well as a network's historical throughput data, to define how video should be encoded to decrease the likelihood of users quitting watching. Unlike previous approaches, the method is independent of the ABR control scheme used by the video player, and the selected ladders perform equivalently across different players with different behaviors. Results of experiments based on real-world network traces demonstrate the usefulness of the proposed method.

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“…The link between emotional and user engagement provides information about what and when (e.g., a part of a video with many arousal peaks) exactly causes a user to feel e.g., aversion, interest or frustration (Picard, 1999). Two parties may particularly benefit from these findings: (a) Social media network providers: the relationships discovered are directly related to the user retention (e.g., user churn rate) (Lebreton and Yamagishi, 2020) and activity (e.g., recommender systems) (Zhou et al, 2016). These are the most common and important tasks of these platforms and are still extremely difficult to model to this day (Lin et al, 2018;Yang et al, 2018;Liu et al, 2019).…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…There are more than 2 billion registered users on YouTube, and a single visitor will remain on the site for at least 10 min (Cooper, 2019). Viewers rate of retention for a single video is between 70-80%, and such retention times may be due to (cross-) social network effects (Roy et al, 2013;Yan et al, 2015;Tan and Zhang, 2019) and the overall improvement in content and connection quality in recent years (Dobrian et al, 2011;Lebreton and Yamagishi, 2020), but arguably caused by intelligent mechanisms (Cheng et al, 2013), e.g., 70% of videos watched on YouTube are recommended from the previous video (Cooper, 2019). To this end, gaining a better understanding of what aspects of a video a user engages with has numerous real-life applications (Dobrian et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Online Video User Engagement From Features of Time- and Value-Continuous, Dimensional Emotions

Stappen¹,

Baird²,

Lienhart³

et al. 2022

Front. Comput. Sci.

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Portraying emotion and trustworthiness is known to increase the appeal of video content. However, the causal relationship between these signals and online user engagement is not well understood. This limited understanding is partly due to a scarcity in emotionally annotated data and the varied modalities which express user engagement online. In this contribution, we utilize a large dataset of YouTube review videos which includes ca. 600 h of dimensional arousal, valence and trustworthiness annotations. We investigate features extracted from these signals against various user engagement indicators including views, like/dislike ratio, as well as the sentiment of comments. In doing so, we identify the positive and negative influences which single features have, as well as interpretable patterns in each dimension which relate to user engagement. Our results demonstrate that smaller boundary ranges and fluctuations for arousal lead to an increase in user engagement. Furthermore, the extracted time-series features reveal significant (p < 0.05) correlations for each dimension, such as, count below signal mean (arousal), number of peaks (valence), and absolute energy (trustworthiness). From this, an effective combination of features is outlined for approaches aiming to automatically predict several user engagement indicators. In a user engagement prediction paradigm we compare all features against semi-automatic (cross-task), and automatic (task-specific) feature selection methods. These selected feature sets appear to outperform the usage of all features, e.g., using all features achieves 1.55 likes per day (Lp/d) mean absolute error from valence; this improves through semi-automatic and automatic selection to 1.33 and 1.23 Lp/d, respectively (data mean 9.72 Lp/d with a std. 28.75 Lp/d).

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Predicting User Quitting Ratio in Adaptive Bitrate Video Streaming

Cited by 9 publications

References 50 publications

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Quitting Ratio-Based Bitrate Ladder Selection Mechanism for Adaptive Bitrate Video Streaming

An Estimation of Online Video User Engagement From Features of Time- and Value-Continuous, Dimensional Emotions

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