Right Buddy Makes the Difference: an Early Exploration of Social Relation Analysis in Multimedia...
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Right Buddy Makes the Difference: an Early Exploration of Social Relation Analysis in Multimedia Applications Jitao Sang, Changsheng Xu*. 1 Institute of
Right Buddy Makes the Difference: an Early Exploration of
Social Relation Analysis in Multimedia Applications Jitao Sang,
Changsheng Xu*. 1 Institute of Automation, Chinese Academy of
Sciences, Beijing, 100190. 2 China-Singapore Institute of Digital
Media, Singapore, 139951. Large scale social media data has
provided opportunities to multimedia research and applications.
Social relation analysis is important for social media applications
in multimedia. Social relation includes group-wise &
peer-to-peer. Peer-to-peer further divides into two-way &
one-way. In social media, typical two-way relation is friendship,
like connect in LinkedIn; typical one-way relation is influence,
such as follow in Twitter, contact in Flickr. We focus influence
analysis of Flickr in this work, and exploit it for application of
personalized search. Introduction For multimedia application,
influence affects someone on behaviors and decisions. We claim that
fixed binary or continuous influence is not enough, and influence
needs to be topic-sensitive. Figure 1. Toy example. Application
Figure 2. The framework {jtsang, csxu}@nlpr.ia.ac.cn
Topic-sensitive Influence Modeling Figure 3. Data analysis:
tracking interest change after adding contact user image search.
Task (query) adaptive is realized. The framework is divided into
two stages: influence modeling and application. For influence
modeling, we simultaneously obtain: (1) topic space (2) user exper-
-tise distribution and (3) topic-sensitive influence. For
application, we employ derived influence to social network-base
personalized (a) before adding contact(b) after adding contact (6
months)(c) the quotient Assumption Inspired by Fig.3, we assume
that user tagging and uploading in two ways: Innovative, create
content based on own interest; Influenced, data generation is
affected by contact users. Figure 4. Graphical representation for
mmTIM Model learning Parameter estimation Discovered topic
illustration Topic #2 traveltriplandscapevacationarchitecture
0.014330.011630.008670.006810.00645 0.37570.34530.26570.24810.1755
Topic #13 fashiondressmodelportraitstyle
0.012130.007020.005520.004860.00461 0.26270.24430.20150.15780.1204
Topic number selection Qualitative case study Figure 6.
Topic-sensitive influencer identification case study
BasicBasic_ACSocial_fixedlSocial_topic Figure 7. Topic-sensitive
influencer identification performance comparison Figure 9. mMAP for
the examined applications (a) Personalized image search(b)
topic-based image recommendation Quantitative evaluation The
identified influencers have high #follower and show strong
expertise on the corresponding topics. mmTIM shows its capability
in identifying the most topic-sensitive influential contact users.
We compare with two topic-level influence analysis methods designed
for text-based networks. Shown in Fig.7, mmTIM consistently
outperforms the two baselines. Figure 8. Generative process of
query q and image d in personalized image search Evaluation results
on applications of personalized image search and topic-based image
recommendation are shown in Fig.9. Fig.9(a) demonstrates the
advantage of topic-sensitive over fixed and no influence. Fig.9(b)
validates our motivation that more accurate influence modeling
contributes to better application performance. References: [1]
Jinfeng Zhuang, et al. Modeling social strength in social media
community via kernel-based learning. ACM MM 2011. [2] Lu Liu, et
al. Mining topic-level influence in heterogeneous networks. In
CIKM, 2010.