Generation process Draw switch variable from bernoulli distribution: ; If

1. Right Buddy Makes the Difference: an Early Exploration of Social Relation Analysis in Multimedia Applications Jitao Sang, ChangshengXu*. 1 Institute of Automation, Chinese Academy of Sciences, Beijing, 100190. 2 China-Singapore Institute of Digital Media, Singapore, 139951. Topic #2 travel vacation landscape trip architecture 0.01433 0.01163 0.00867 0.00681 0.00645 0.3757 0.3453 0.2657 0.2481 0.1755 Topic #13 fashion portrait model dress style 0.01213 0.00702 0.00552 0.00486 0.00461 0.2627 0.2443 0.2015 0.1578 0.1204 {jtsang, csxu}@nlpr.ia.ac.cn • 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. Topic-sensitive Influence Modeling Qualitative case study • 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. • 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. • Generation process • Draw switch variable from bernoulli • distribution: ; • If • Draw influencer from ’s contact • list: ; • Draw topic from ’s topic • distribution: ; • If • Draw topic from ’s own topic • distribution: ; • Draw word from topic-word • distribution: . Figure 6. Topic-sensitive influencer identification case study Quantitative evaluation We compare with two topic-level influence analysis methods designed for text-based networks. Shown in Fig.7, mmTIMconsistently outperforms the two baselines. 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. Application Figure 4. Graphical representation for mmTIM Model learning Figure 8. Generative process of query q and image d in personalized image search Figure 1. Toy example. • Extend risk minimization framework to personalized image search: • Define LMs on the derived topic space • Consider user into query LM and risk calculation • Topic-sensitive influence serves as weight to balance risks from searcher and the influencers . Parameter estimation , Topic number selection 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 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. We choose the smallest that yields small perplexity and fast convergence: . Figure 5. Perplexity over iterations for different topic number Discovered topic illustration References: [1]JinfengZhuang, 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. Figure 2. The framework image search. Task (query) adaptive is realized. Basic Basic_AC Social_fixedl Social_topic (a) before adding ‘contact’ (b) after adding ‘contact’ (6 months) (c) the quotient (a) Personalized image search (b) topic-based image recommendation Figure 7. Topic-sensitive influencer identification performance comparison Figure 3. Data analysis: tracking interest change after adding contact user Figure 9. mMAP for the examined applications