Renjie Zhou

Bio: Renjie Zhou is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Computer science & Click-through rate. The author has an hindex of 5, co-authored 5 publications receiving 388 citations. Previous affiliations of Renjie Zhou include Harbin Engineering University.

The impact of YouTube recommendation system on video views

Abstract: Hosting a collection of millions of videos, YouTube offers several features to help users discover the videos of their interest. For example, YouTube provides video search, related video recommendation and front page highlight. The understanding of how these features drive video views is useful for creating a strategy to drive video popularity. In this paper, we perform a measurement study on data sets crawled from YouTube and find that the related video recommendation, which recommends the videos that are related to the video a user is watching, is one of the most important view sources of videos. Despite the fact that the YouTube video search is the number one source of views in aggregation, the related video recommendation is the main source of views for the majority of the videos on YouTube. Furthermore, our results reveal that there is a strong correlation between the view count of a video and the average view count of its top referrer videos. This implies that a video has a higher chance to become popular when it is placed on the related video recommendation lists of popular videos. We also find that the click through rate from a video to its related videos is high and the position of a video in a related video list plays a critical role in the click through rate. Finally, our evaluation of the impact of the related video recommendation system on the diversity of video views indicates that the current recommendation system helps to increase the diversity of video views in aggregation.

How YouTube videos are discovered and its impact on video views

Abstract: As the largest video sharing site around the world, YouTube has been changing the way people entertain, gain popularity, and advertise. Discovering the major sources that drive views to a video and understanding how they impact the view growth pattern have become interesting topics for researchers as well as advertisers, media companies, or anyone who wish to have a shortcut to stardom. The work of this paper is to identify three major view sources, related video recommendation, YouTube search, and video highlight such as popular video list on YouTube homepage or video embedding on social networking sites, and examine the patterns of views from each view source. First, the impact of each view source on the view diversity and on the view share of each individual video is analyzed. It is found that while search and highlight create an effect of rich-get-richer, the related video recommendation equalizes the view distribution and helps users find niche videos. Second, the contribution of the three view sources to video popularity growth is investigated. The investigation reveals that search and related video recommendation are the two major sources that persistently drive views to a video. The view rates from recommendation and search are generally stabilized to be constant view rates. Third, the underlying factors that affect the long-term view rate from referrer videos are explored. The results indicate that the top referrer video set of a video is fairly stable and the view rate from recommendation is mainly determined by view rates of top referrer videos. Finally, whether highlight increases the view rate of a video after the duration of promotion is studied. The observations suggest that video highlight does not directly impact the view rate of a video after the event finishes. The findings presented in the paper provide several key insights into the impact and patterns of view contributions for each major source of the video views.

Watching user generated videos with prefetching

Abstract: Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by showing that video playbacks of videos of YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

Watching user generated videos with prefetching

Abstract: Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by performing a PlanetLab-based measurement demonstrating that video playback on YouTube is often unsatisfactory and introduce a series of prefetching schemes: (1) the conventional caching scheme, which caches all the videos that users have watched, (2) the search result-based prefetching scheme, which prefetches videos that are in the search results of users' search queries, and (3) the recommendation-aware prefetching scheme, which prefetches videos that are in the recommendation lists of the videos that users watch. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio of up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates strong potential for improving the playback quality at the client. In addition, we explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

Boosting video popularity through recommendation systems

Abstract: While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity.

Science communication on YouTube: factors that affect channel and video popularity

Abstract: YouTube has become one of the largest websites on the Internet. Among its many genres, both professional and amateur science communicators compete for audience attention. This article provides the first overview of science communication on YouTube and examines content factors that affect the popularity of science communication videos on the site. A content analysis of 390 videos from 39 YouTube channels was conducted. Although professionally generated content is superior in number, user-generated content was significantly more popular. Furthermore, videos that had consistent science communicators were more popular than those without a regular communicator. This study represents an important first step to understand content factors, which increases the channel and video popularity of science communication on YouTube.

A survey on predicting the popularity of web content

Abstract: Social media platforms have democratized the process of web content creation allowing mere consumers to become creators and distributors of content. But this has also contributed to an explosive growth of information and has intensified the online competition for users attention, since only a small number of items become popular while the rest remain unknown. Understanding what makes one item more popular than another, observing its popularity dynamics, and being able to predict its popularity has thus attracted a lot of interest in the past few years. Predicting the popularity of web content is useful in many areas such as network dimensioning (e.g., caching and replication), online marketing (e.g., recommendation systems and media advertising), or real-world outcome prediction (e.g., economical trends). In this survey, we review the current findings on web content popularity prediction. We describe the different popularity prediction models, present the features that have shown good predictive capabilities, and reveal factors known to influence web content popularity.

Scaling social media applications into geo-distributed clouds

Abstract: Federation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a Δ(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform.

The untold story of the clones: content-agnostic factors that impact YouTube video popularity

Abstract: Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity.In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.