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  1. 1. SFViz Interest-based Friends Exploration and Recommendation in Social NetworksLiang Gou, Fang You, Jun Guo, Luqi Wu, Xiaolong (Luke) Zhang College of Information Science & Technology The Penn State University School of Communication and Design Sun Yat-Sen University
  2. 2. How to recommend friends by consideringboth existing social connections of a personand her dynamic interests?Is there a visual exploration wayto dynamically recommendfriends by considering both socialconnections and a context ofsocial connections (e.g., similarinterest)?
  3. 3. Our Approach• In this paper, we propose a novel visualization tool to support users to explore and find friends interactively with different interests. – Social networks: measure how close people are – Social tags: measure the interest similarity
  4. 4. Overview• Reviews relevant literature on tag visualization and social recommendation visualization, profile-based and topology- based recommendation approaches.• Presents our framework and a hybrid approach of social tags and social networks.• Describes the visualization design and implementation of a system, SFViz, to support user interaction and exploration.• Illustrates a case study of using SFViz for friend exploration and recommendation in a music community, Last.fm.
  5. 5. About SFViz- SFViz Framework
  6. 6. About SFViz- SFViz Framework• Extract the information about user dynamicinterest based on tags created by users.• Construct tag networks to reflect a user’sinterest and a hierarchical tag structure .• Calculate similarities among people with theinformation of tag networks and socialnetworks to recommend friends for users.
  7. 7. Generate a Matched Compound Graph• Matching Score: measures how appropriate an actor is assigned to the smallest category (the deepest non-leaf node) defined in the tag hierarchy. Root 10 11 8 9 Depth=1 C1 C2 C3 7 Depth=2C11 C12 C 21 C 22 C 31 C 32 C 33 6 5 Depth=3 1 2 3 4 t1 t2 t3 ms(3,C21)=? { t1 t7 C21 C2 }
  8. 8. Calculate Actor Similarity
  9. 9. Visualization DesignTo help users explore and interact recommended friends ina compound graph, SFViz design support:• Tag tree exploration and interaction, showing contextand details information (parent-child relation, siblingsnodes);• Social network exploration and interaction, showinghighly connected cliques, direct friends, a critical path to afriend and so on;• Friend recommendation with context of interest, showingpotential friends with specified interests with differentgranularities in a tag tree and how to reach these users.
  10. 10. Visualization DesignWith SFViz framework, we need transform a matchedcompound graph consisting of two sub-graphs of tree andnetwork, and social recommendation into visual forms.To meet these requirements, we design and implementSFViz with several key visualization techniques:• Radial, Space-Filling (RSF) technique to visualize atag tree.• Circle layout with edge bundling to show a socialnetwork, highlighted social recommendation views andseveral interactions.
  11. 11. Layout a Tag Tree with RSFThe tag tree is represented with a Radial, Space-Filling (RSF) techniquein RSFViz. The RSF uses nested circles to show the parent-childrelationship: the root node in the centre of a circle and child nodesplaced within the arc subtended by their parents.• Tag nodes from a depth are assignedalong a circle with color showing theirdepth. The tree hierarchy information isshown with inclusion relationship in therepresentation.• Node width in a circle is proportional tothe count of all its children and leaf nodeshave a uniform size.• Node width can be adjusted to showmore or less details of this node and itsdescendants.
  12. 12. Circularly Layout Social Network • Nodes in a social network are also the leaf nodes in the tag tree in a matched compound graph. To reflect the matched relationship, we use a RSF tree as the supporting structure and layout a social network over the RSF tree. The idea is to circularly arrange the network nodes to corresponding positions in the circle outlined by the RSF tree, and then connects the node sectors within the circle. This design integrates both network and tree structures in a single graph without introducing extra nodes and links.
  13. 13. Case study• Dataset: The dataset in our case study is from a social music service Last.fm retrieved by Multimedia Information Retrieval Group at Glasgow University in November 2008.• Data Preprocessing• Tag-Based Multiscale and Cross-scale Exploration.• Friend Recommendation Exploration.
  14. 14. Case study (Cont.)Tag network of LastFm data after filtering. Tag (category) tree with RSF representation.
  15. 15. Friend Recommendation Exploration A view of share friends without aggregation.A view of share friends with aggregation.
  16. 16. This is an on going work• We will extend the work in two directions. First, we will conduct more experiments and user studies of our approach. The experiments will assess the accuracy our recommendation with some labelled dataset, and in user studies, we may ask real users to rate friends recommendation.• Second, we will incorporate other methods and information to classify users to a tag hierarchy, such as user’s profile information.
  17. 17. Can I take questions?
  18. 18. Thank you for your attention!
  19. 19. Reference• Gou, L., Zhang, S. K., Wang, J. & Zhang, X. L. (2010). TagNetLens: Visualizing knowledge structures with social tags. In Proc. of ACM VINCI’10, 18: 1-9.• Marsh, D.R., Schroeder, D.G., Dearden, K.A., Sternin, J. & Sternin, M.(2004). The power of positive deviance. British Medical Journal, 329, pp. 1177–1179.• Israel, B.A. (1982). Social networks and health status: Linking theory, research and practice. Patient Counseling and Health Education, 4(2), pp. 65-77.• Davis, H, Vetere, F, Ashkanasy, et al. (2008) Towards Social Connection for Young People With Cancer. OzCHI, Queensland.• Goswami, S., Köbler, F., Leimeister, J. M. & Krcmar, H. (2010). Using online social networking to enhance social connectedness and social support for the elderly. In Proc. of ICIS’10, pp. 109-120.• Lampe, C., Ellison, N. & Steinfield, C. (2007). A familiar Face(book): Profile elements as signals in an online social network. In Proc. of CHI’07, pp. 435-444.• Krulwich, B. (1997). Lifestyle finder: intelligent user profiling using large-scale demographic data. Artificial Intelligence Magazine, 18(2), pp. 37–45.• Mooney, R. J. & Roy, L. (2000). Content-based book recommending using learning for text categorization. In Proc. of DL’00, pp. 195-204.