Personalized Celebrity Video Search Based on Cross-space Mining - PowerPoint Presentation

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Personalized Celebrity Video Search Based on Cross-space Mining

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Zhengyu Deng, Jitao

Sang, Changsheng Xu

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Chinese-Singapore Institute of Digital Media

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Institute of Automation, Chinese Academy of Sciences

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Outline

Motivation

Framework

Approach

Experiment

Conclusions

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Motivation

Celebrities are often popular in multiple fields and user interests are diverse.

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User 1

User 2

User 3

Sports video

Entertainment video

Interview video

Beckham

like

like

like

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Motivation

Celebrities are often popular in multiple fields and user interests are diverse.

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User

Sports video

Entertainment video

Music video

like

like

like

Beckham

Bieber

Lady Gaga

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Daily life

Interview

Sports

David Beckham

Non-personalized search

Motivation

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Problem and solution

Motivation

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User

Celebrity

Interest space

Popularity space

Map

R

e-rank

Topic

modeling

Topic

modeling

2014/8/30

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Framework

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Approach

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… … … … …

U1

Z1

U2

U

m

User

Interest Space

Z2

Zp

Popularity Space

C1

C2

Cn

Celebrity

Xq

W1

W2

Wx

Vocabulary

X1

X2

LDA

LDA

Random walk

P(

W

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P(

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P(

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P(

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KL-Divergence

P(

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Random walk

V

j is the initial probabilistic score; pij is the transition matrix; rk(j) donote the relvence score of node j at iteration k (1) Rewrite as (2)The unique solution (3)

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Approach

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KL-Divergence

() is from interest (popularity) space. The KL-Divergence between them is (4)where denote the distribution score of topic on word .The similarity of topic and is defined as the inverse of KL-Divergence. (5)

 

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Approach

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Video Projection

Given a celebrity video , project it to interest space (6)where K is the topic number of interest space. M is the dimension of the vocabulary.

 

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Approach

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Video re-ranking

Given a user and celebrity , the score of is (7) = =where K(L) is the topic number of interest (popularity) space, () is the th th topic of interest (popularity) space, is approximated by the inverse of KL-Divergence.

 

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Approach

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Data Preparation

Celebrity listThe World's Most Powerful 100 Celebrities Listhttp://www.forbes.com/wealth/celebrities/listThe 30 Most Generous Celebritieshttp://www.forbes.com/sites/andersonantunes/2012/01/11/the-30-most-generous-celebrities/3/Top 200 Sexiest Actorhttp://www.imdb.com/list/Uun6vT7hWeM/For each celebrity, 200 videos are downloaded from YouTube.

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Experiments

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User and Celebrity Profiling

User  registration info., favorite and uploaded videos  raw tags  stop words  WorldNet  noun tags.Celebrity  Wikipedia Entry  WorldNet  noun tags

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celebrityusertotalSize286200486Tags Number11424583312073

Experiments

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Experimental Setting

Experiment data143 users106 celebritiesExperiment setupEach user have some videos related with a specific celebrity. Leave this videos out and learn topics.Rank this celebrity’s videos for the user.Evaluationf-Measure

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Experiments

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Topic simples

Experiments

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Doc-Topics distribution

E.g. Celebrity ”Beckham” Topic Probability of appearance 7 0.6229086229086229 4 0.1956241956241956 0 0.04967824967824968 8 0.03963963963963964 3 0.022393822393822392 1 0.018532818532818532 6 0.017245817245817245 9 0.014414414414414415 5 0.014414414414414415 2 0.005148005148005148

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Experiments

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E.g. <topic id=“7"> <word weight="0.018062955825114312" count="478">jay</word> <word weight="0.01726939500434569" count="457">messi</word> <word weight="0.016891508899217776" count="447">real</word> <word weight="0.016551411404602652" count="438">ronaldo</word> <word weight="0.01640025696255149" count="434">kanye</word> <word weight="0.015644484752295656" count="414">west</word> <word weight="0.015606696141782866" count="413">wayne</word> <word weight="0.014964289763065412" count="396">lil</word> <word weight="0.013414956732040963" count="355">hop</word> <word weight="0.013226013679477006" count="350">lionel</word> <word weight="0.01311264784793863" count="347">beckham</word> <word weight="0.01231908702717001" count="326">beyonce</word> <word weight="0.012054566753580472" count="319">cristiano</word> <word weight="0.011941200922042096" count="316">soccer</word> <word weight="0.011941200922042096" count="316">football</word> … …

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Topic-terms distribution

Experiments

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E.g. <topic id=“4"> <word weight="0.026509629402286503" count="1382">show</word> <word weight="0.014904473260185682" count="777">david</word> <word weight="0.014444103429755236" count="753">ellen</word> <word weight="0.01430982889587969" count="746">tv</word> <word weight="0.012027161819995396" count="627">comedy</word> <word weight="0.01112560423540244" count="580">jennifer</word> <word weight="0.010857055167651347" count="566">interview</word> <word weight="0.010550141947364382" count="550">degeneres</word> <word weight="0.010166500422005677" count="530">funny</word> <word weight="0.009245760761144787" count="482">letterman</word> <word weight="0.008689480549374665" count="453">hollywood</word> <word weight="0.008497659786695312" count="443">late</word> <word weight="0.007979743727461061" count="416">talk</word> <word weight="0.007615284278370291" count="397">celebrity</word> <word weight="0.006943911608992557" count="362">television</word> … …

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Topic-terms distribution

Experiments

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Different approaches

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Experiments

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Impact of random walk

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Experiments

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Conclusions

ConclusionsWe presented a cross-space mining method to exploit the correlation between user preferences and celebrity popularities.Future workInstead of returning a ranking list, we will try to visualize the search results into semantically consistent groups.Investigate the issue of personalized query understanding in more general personalized search applications.

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Thank you!Q&A?

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