• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content

Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

Like this presentation? Why not share!

Temporal recommendation on graphs via long and short-term

on

  • 2,817 views

 

Statistics

Views

Total Views
2,817
Views on SlideShare
2,147
Embed Views
670

Actions

Likes
5
Downloads
133
Comments
0

9 Embeds 670

http://xlvector.net 645
http://www.linkedin.com 9
http://ad-research.appspot.com 7
http://xianguo.com 3
http://static.slidesharecdn.com 2
http://translate.googleusercontent.com 1
http://feeds.feedburner.com 1
http://www.zhuaxia.com 1
https://www.linkedin.com 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Temporal recommendation on graphs via long  and short-term Temporal recommendation on graphs via long and short-term Presentation Transcript

    • Temporal recommendation on graphs via long- and short-term preference fusion
      Liang Xiang
      xlvector@gmail.com
    • Main Content
      Temporal Recommendation
      Long/short term preference
      Bipartite Graph Model
      Session Graph Model
      Path Fusion Algorithm
    • 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
    • Our Contribution
      Temporal Recommendation on Graph Model
      Implicit feedback data
      Combine Long/short term interest together
      Graph Model
      Temporal Recommendation
    • Long/Short Term Preference
      Short-term Preference
      Long-term Preference
    • 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
    • Session Graph Model
    • 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
    • Session Graph Model
      Session Node
      Item Node
      User Node
    • Ranking and Recommendation
    • 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]
    • Path Fusion Ranking
      a
      A
      b
      B
      c
    • 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
    • Experiments
    • Experiments
    • Experiments
    • This model does not work in every system!
      Future work
    • Temporal Effectiveness
      Slow Evolution System
      Session Graph Model Perform Good
      Fast Evolution System
      Session Graph Model Perform Bad
    • Temporal Effectiveness
    • 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