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  • 1. Temporal recommendation on graphs via long- and short-term preference fusion
    Liang Xiang
    xlvector@gmail.com
  • 2. Main Content
    Temporal Recommendation
    Long/short term preference
    Bipartite Graph Model
    Session Graph Model
    Path Fusion Algorithm
  • 3. Related Works
    Neighborhood Model [Ding CIKM05]
    Users future preference is mainly dependent on their recent behavior
    Latent Factor Model [Koren KDD09]
    User bias shifting
    Item bias shifting
    User preference shifting
    Seasonal effects
  • 4. Our Contribution
    Temporal Recommendation on Graph Model
    Implicit feedback data
    Combine Long/short term interest together
    Graph Model
    Temporal Recommendation
  • 5. Long/Short Term Preference
    Short-term Preference
    Long-term Preference
  • 6. Long/Short Term Preference
    Long term preference
    Personal preference
    Do not change frequently
    Last for long period
    Short term preference
    Influenced by social event
    Change frequently
    May be become long term preference
  • 7. Session Graph Model
  • 8. Session Graph Model
    Session Node
    User Node
    Item Node
    a
    A
    b
    A
    (A,a,1) (A,c,2)
    (B,b,1) (B,c,2)
    B
    c
    a
    A:1
    A:2
    b
    B
    c
    B:1
    B:2
    Bipartite Graph Model
    Session Graph Model
  • 9. Session Graph Model
    Session Node
    Item Node
    User Node
  • 10. Ranking and Recommendation
  • 11. Path Fusion Ranking
    Two nodes in a graph have large similarity if:
    There are many paths between two nodes;
    These paths have short length;
    Most of these paths do not contains nodes with large out degree.
    [YouTube WWW2008]
  • 12. Path Fusion Ranking
    a
    A
    b
    B
    c
  • 13. Path Fusion Ranking
    Implement by Breath-First-Search
    Fast and low space complexity
    Its speed dependents on graph sparsity;
    It can be speed up by randomly select edges;
    Do not need to store user-user or item-item similarity matrix
    Easy to do incremental update
    New data can insert into graph directly;
    After graph is updated, recommendation result will be changed immediately
  • 14. Experiments
  • 15. Experiments
  • 16. Experiments
  • 17. This model does not work in every system!
    Future work
  • 18. Temporal Effectiveness
    Slow Evolution System
    Session Graph Model Perform Good
    Fast Evolution System
    Session Graph Model Perform Bad
  • 19. Temporal Effectiveness
  • 20. Solution
    Add Item Session Node
    a
    A
    A
    b
    A
    a
    B
    A:1
    c
    a
    a:1
    A:1
    A:2
    b
    A:2
    b
    B
    b:1
    B
    c
    B:1
    c
    B:1
    (A,a,1) (A,c,2)
    (B,b,1) (B,c,2)
    c:2
    B:2
    B:2