Mining Cross-network Association for YouTube

Video Promotion

Ming Yan

Institute of Automation, Chinese Academy of Sciences

May 15, 2014

Outline

Motivation

Three-stage Framework

Some Visualization

Further Discussion

Background

• More than 1 billion unique users visit YouTube each month.

• Over 6 billion hours of video are watched each month on YouTube.

• 100 hours of video are uploaded to YouTube every minute.

• Large quantities of videos are consumed in YouTube and the trend is growing year by year.

• YouTube exhibits limited propagation efficiency and many videos remain unknown to the wide public.

• Long tail effect for the video view count distribution.

• Short active life span for most videos.

Background

• YouTube video popularity limited by its internal mechanism.• Internal search• Related video recommendation• Channel subscription• Front page highlight

• External referrers such as social media websites arise to be important sources to lead users to YouTube videos.

• Twitter has been quickly growing as the top referrer source for web video discovery.

Motivation

YouTube video

Twitter followee

watch

Twitter follower

• For specific YouTube video, to identify proper Twitter followees with goal to maximize video dissemination to the followers.

Got 1 billion views in 5 months

Challenge

• The heterogeneous knowledge association between YouTube video and Twitter followee

• user-perceived

• How to define the “properness” of candidate Twitter followee for a specific YouTube video

• interestness• virtual cost

Our Twitter followee identification scheme actually expects to find the optimal Twitter followee whose followers are more likely to show interest to the target video.

User-perceived Solution

• Illustration example

viewview favor

follow

User Association

follow

better promotion referrer

Framework

• Three Stages

Heterogeneous Topic Modeling

YouTube videos

𝒇 𝒘

iCorr-LDA𝒗

𝒖 LDA

Twitter users

Twitter user distribution

𝑝 (𝒛 𝑇∨𝑢)…

𝑝 (𝒛 𝑌∨𝑣 )

YouTube video distribution

ACM Multimedia 2014@acmmm14

Bill Gates@BillGates

NBA@NBA

Britney Spears@britneyspearsUsername

@TwitterID 𝓤𝒖𝒇𝒐𝒍𝒍𝒐𝒘𝒆𝒆…

…

FollowingInput• YouTube video : []• Twitter users with their follower setOutput• Twitter user distribution • YouTube user distribution

𝒗

Topic Modeling Approach• On YouTube Side:Propose an inverse Corr-LDA model to discover the YouTube video multimodal topics.

• On Twitter Side:Standard LDA on Twitter followee-follower social graph.

user as document user’s followees as word 𝛼 𝜃 𝑧 𝑤

𝑓𝑦

𝑀

𝑁

¿𝒱∨¿𝛽

𝜎𝜇

Cross-network Topic Association

YouTube user distribution 𝑝 (𝒛 𝑌∨𝑢)…

Association Mining

Aggregation

𝓤𝑌𝓤𝑇𝓤𝑜

overlapped users

𝒛 𝑇

𝒛 𝑌

𝒖❑𝑇

𝒖❑𝑌

Interested videos

username𝓥𝑢

𝓤𝑜

𝑈𝑇

𝑉

Input• Twitter user and video distribution and (output of stage 1)• YouTube, Twitter and the overlapped

user set • YouTube user interested video set

Output• Distribution transfer function

(: the aggregated YouTube user distribution)

Approach• YouTube User Aggregation• Association Mining

Cross-network Topic Association

• YouTube User Aggregation

…

𝒖user ’s interested videos

𝒗𝟏

𝒗𝟐

𝒗𝒏

𝑤1

𝑤2

𝑤𝑛

𝑝 (𝑧𝑘|𝑢𝑖 )= ∑𝑣∈𝑉𝑢

𝑁𝑣 ( 𝑓 )+𝑁𝑣 (𝑤)𝑁 ( 𝑓 )+𝑁 (𝑤)

∙𝑝 (𝑧𝑘𝑌∨𝑣)

: the total number of keyframes and words in video : the total number of keyframes and words in ’s video set

𝒗

𝑝 (𝑧𝑘𝑌∨𝑣 )

𝑝 (𝑧𝑘|𝑢𝑖 )

Cross-network Topic Association

• Association Mining

Association Mining

𝓤𝑌𝓤𝑇𝓤𝑜

overlapped users

𝒛 𝑇

𝒛 𝑌

𝓤𝑜

Goal: • To obtain the association between the YouTube video

space and Twitter user space. (i.e. )

Approach:• Transition Probability-based Association• Regression-based Association• Latent Attribute-based Association

Explicit association/transition matrix: 𝐴

Cross-network Topic Association

• Transition Probability-based Association

• Regression-based Association

q=1: lasso problem and can be effectively solved by LARS and feature sign algorithmq=2: ridge regression problem and with analytical solution as

The overlapped users’ distribution matrix in Twitter and YouTube

Cross-network Topic Association

• Latent Attribute-based Association (non-linear)• only on overlapped users• on all users

• Innovation: To discover shared latent structure behind the two topic spaces. (After projected to the latent attribute spaces, user’s YouTube and Twitter distribution share the same coefficient.)

shared latent user attribute• Only on overlapped users

By some simple transfer, it can be efficiently solved by the sparse coding algorithm.

Cross-network Topic Association

• Latent attribute discovery on all users (plenty of non-overlapped users are considered in this scheme)

• Objective function

𝑆𝑌=[𝑆𝑜 ,𝑆𝑛𝑜𝑛𝑌 ] ,𝑆𝑇=[𝑆𝑜 ,𝑆𝑛𝑜𝑛

𝑇 ]

• Iteratively solved via three sub-problems

Referrer Identification

𝒘𝒗 𝒕

𝒇

test YouTube video

𝑝 (𝒛 𝑌∨𝑢𝑡)

Distribution Transfer

𝑝 (𝒛 𝑇∨�̂�𝑡)

candidate Twitter followees

𝓤𝑡𝑓𝑜𝑙𝑙𝑜𝑤𝑒𝑒

Matching

…

Input• Distribution transfer function • Test videos • Twitter followee set

𝒗 𝒕

Output• Twitter followee rank for each video

𝒗 𝒕

Approach• Direct product-based matching• Weighted product-based matching

Referrer Identification

• Direct product-based matching

• Weighted product-based matching• Ranking SVM algorithm is used to train the weights:• Feature:

• Training label: a designed properness score

• With the learnt model parameter

In charge of the coverage of the interested audiences

In charge of the virtual cost

Some Visualization

Further Discussion

Some Extensible ApplicationExamining the value of Twitter followees (Our

work can be viewed as valuing Twitter followee w.r.t. promotion efficiency to YouTube videos)

(e.g. the followee has a lot of young female followers)

Advertising (Advertising media selection for our work)

(e.g. anchor text generation (i.e., optimizing video description for promotion), advertising slot bid (i.e., followee reshare time selection))

Other user-bridged cross network application

Tweet Topic

TaobaoTopicuser

ChallengeData hard to get!

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