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, China-Singapore Institute of Digital Media, Singapore, 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, 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 Topic #13 fashiondressmodelportraitstyle 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 [2] Lu Liu, et al. Mining topic-level influence in heterogeneous networks. In CIKM, 2010.