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SFViz    Interest-based Friends Exploration and     Recommendation in Social NetworksLiang Gou, Fang You, Jun Guo, Luqi Wu...
How to recommend friends by consideringboth existing social connections of a personand her dynamic interests?Is there a vi...
Our Approach• In this paper, we propose a novel visualization  tool to support users to explore and find friends  interact...
Overview• Reviews relevant literature on tag visualization and social  recommendation visualization, profile-based and top...
About SFViz- SFViz Framework
About SFViz- SFViz Framework• Extract the information about user dynamicinterest based on tags created by users.• Construc...
Generate a Matched Compound           Graph• Matching Score: measures how appropriate an actor is  assigned to the smalles...
Calculate Actor Similarity
Visualization DesignTo help users explore and interact recommended friends ina compound graph, SFViz design support:• Tag ...
Visualization DesignWith SFViz framework, we need transform a matchedcompound graph consisting of two sub-graphs of tree a...
Layout a Tag Tree with RSFThe tag tree is represented with a Radial, Space-Filling (RSF) techniquein RSFViz. The RSF uses ...
Circularly Layout Social Network         • Nodes in a social network are also the         leaf nodes in the tag tree in a ...
Case study• Dataset: The dataset in our case study is from a social  music service Last.fm retrieved by Multimedia  Inform...
Case study (Cont.)Tag network of LastFm data after filtering.                                              Tag (category) ...
Friend Recommendation                   Exploration                                            A view of share friends wit...
This is an on going work• We will extend the work in two directions. First, we will  conduct more experiments and user stu...
Can I take questions?
Thank you for your attention!
Reference•   Gou, L., Zhang, S. K., Wang, J. & Zhang, X. L. (2010). TagNetLens: Visualizing knowledge structures    with s...
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Vinci2011会议演讲PPT

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  • Friend recommendation is popular in social network services to help people make new friends and expand their networks. Friend recommendation is either based on topological structures of a social network, or derived from profile information of users. However, dynamically recommending friends by considering both social connections and a context of social connections (e.g., similar interest) in a way of visual exploration is not well supported by existing tools.
  • How to recommend friends by considering both existing social connections of a person and her dynamic interests? In this paper, we propose a novel approach to support users to explore and find friends interactively with different interests
  • This paper is organized as the following. Section 2 reviews relevant literature on tag visualization and social recommendation visualization, profile-based and topology-based recommendation approaches. Section 3 presents our framework and a hybrid approach of social tags and social networks. Section 4 describes the visualization design and implementation of a system, SFViz, to support user interaction and exploration. Section 5 illustrates a case study of using SFViz for friend exploration and recommendation in a music community, Last.fm. The paper concludes with the discussion on the findings and implications of our research, and future research directions.
  • We design a visualization system, SFViz (Social Friends Visualization), to help users explore recommendation with different interests in an interactively way. SFViz framework is shown in Figure 1. This framework follows the idea of the reference model by Card et al. [38]. On the top, the data model takes social networks and social tags as input and then data model is transformed into visual forms to users for exploration and interaction. Users can manipulate both data model and visual forms on their demand. In data model, social tags are converted to a tag network (Section 3.1) and then a tag hierarchy is created with the tag network (Section 3.2). People in social networks are matched to the tag hierarchy. This results in a compound graph including both tree and network structure (Section 3.4). Then, actor similarity under a specified context of interest is calculated to recommend friends to users (Section 3.5
  • More specifically, we first extract the information about user dynamic interest based on tags created by users in a social network service system. We construct tag networks to reflect a user’s interest and a hierarchical tag structure implying a knowledge structure shared among those people who generated them. Then, we calculate similarities among people with the information of tag networks and social networks to recommend friends for users.
  • 𝐴𝑆 (𝑖,𝑗)=cos(𝑖,𝑗)=(𝑣_𝑖⋅𝑣_𝑗)⁄(|𝑣_𝑖 |⋅|𝑣_𝑗 |)
  • With SFViz framework shown in Figure 1 (Section 3.1), we need transform a matched compound graph consisting of two sub-graphs of tree and network, and social recommendation into visual forms. To meet these requirements, we design and implement SFViz with several key visualization techniques: a Radial, Space-Filling (RSF) technique to visualize a tag tree, a circle layout with edge bundling to show a social network, highlighted social recommendation views and several interactions.
  • With SFViz framework shown in Figure 1 (Section 3.1), we need transform a matched compound graph consisting of two sub-graphs of tree and network, and social recommendation into visual forms. To meet these requirements, we design and implement SFViz with several key visualization techniques: a Radial, Space-Filling (RSF) technique to visualize a tag tree, a circle layout with edge bundling to show a social network, highlighted social recommendation views and several interactions.
  • In the part of visual form, a compound graph is visually represented as an integration of RSF tree and circular network (Section 4). Various tools are designed to support user interaction with the compound graph. Recommended friends are visually encoded to the compound graph with similarity scores. Users can change a tag of interest to update recommended friends with the context information. The tag tree is represented with a Radial, Space-Filling (RSF) technique in RSFViz. The RSF uses nested circles to show the parent-child relationship: the root node in the centre of a circle and child nodes placed within the arc subtended by their parents. The RSF technique clearly illustrates the parent-child relationship in the tree and also node area to present node properties [43][44]. Figure 6a is an example of the RSF visualization of a tag tree structure shown in Figure 4a. The root node is placed in the center and shown in transparent. Tag nodes from a depth are assigned along a circle with color showing their depth. The tree hierarchy information is shown with inclusion relationship in the representation. We can also see that node width in a circle is proportional to the count of all its children and leaf nodes have a uniform size. Node width can be adjusted to show more or less details of this node and its descendants.
  • Two examples of circular layout of social networks in SFViz design are shown in Figure 6b and 6c.In Figure 6b, the social network in Figure 4a is circularly placed along in a RSF tree. The aggregated social network of Figure 5 is also shown in Figure 6c. We show expanded parent tags as the context of child tags which are transparent and labelled with grey color. We use the color of tag with a higher level to encode an aggregated edge which collects tags from two different levels. For example, has the same the color of C 1 is used for the edge between node 6 and C 1 because C 1 has a higher scale than node 6. The aggregated weight of an edge is shown with thickness. Both Figure 6b and 6c uses straight lines to show edges in circular layout, which results in a problem that some edges may be occluded by node sectors. For example, the node sector C 1 interrupts the edge between node 6 and C 1 . To alleviate this issue, we introduce a technique of edge bundling in the following section.
  • Illustrates a case study of using SFViz for friend exploration and recommendation in a music community, Last.fm.
  • SFViz can suggest friends to a user based on the user’s social network information and a given interest category in a tag tree. After the user is known (e.g., with log-on information or by specifying from a network), the Top-N recommended people are shown with gradient colors. Figure 18 shows the top 10 recommended friends for the center user who is purple at the left bottom. The recommended people are shown with colors from red to yellow based on their rankings. In this example, no category of interest is specified. When a category of interest is specified in the tag tree, social recommendation will be adjusted dynamically. Figure 19 shows the 10 recommended friends with a tag category of “hip hop” for the same user in Figure 18. We can see that the recommended friends are narrowed down to the category of “hip hop”.
  • Transcript of "Vinci2011会议演讲PPT"

    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.

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