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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 A B a b c (A,a,1) (A,c,2) (B,b,1) (B,c,2) A B a b c A:1 A:2 B:1 B:2 Bipartite Graph Model Session Graph Model Session Node User Node Item Node
- 9. Session Graph Model Session Node User Node Item Node 1 1 1 1 ( ) (1 ) i u uT v v v v v v v
- 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 B a b c 1 1 1 ( ) ( , ) ( ) | ( ) | N i i i i i v w v v weight P out v ( , ') ( , ') ( ) P path v v d v v weight P ( ) ( , ) ( ) ( , ) ( ) ( , ) ( , , , ) | 2 | | 2 | | 2 | A w A c c w c B B w B b weight A c B b
- 13. Path Fusion Ranking 1. Implement by Breath-First-Search 2. Fast and low space complexity a) Its speed dependents on graph sparsity; b) It can be speed up by randomly select edges; c) Do not need to store user-user or item-item similarity matrix 3. Easy to do incremental update a) New data can insert into graph directly; b) 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 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 nytimes youtube wikipedia sourceforge blogspot netflix
- 20. Solution • Add Item Session Node A B a b c A B a b c A:1 A:2 B:1 B:2 A B a b c A:1 A:2 B:1 B:2 a:1 b:1 c:2 (A,a,1) (A,c,2) (B,b,1) (B,c,2)

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