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Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
Collaborative Filtering Based on Star Users
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Collaborative Filtering Based on Star Users

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Collaborative Filtering based on star users. 2011, 23rd ICTAI

Collaborative Filtering based on star users. 2011, 23rd ICTAI

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  • 1. ICTAI 2011, Boca Raton November 7, 2011Collaborative Filtering Based on Star Users Qiang Liu with Bingfei Cheng and Congfu Xu College of Computer Science and Technology Zhejiang University Hangzhou, Zhejiang 310027, China 2012dtd@gmail.com
  • 2. Outline Introduction Star-user-based Collaborative Filtering Experimental Results Conclusion
  • 3. INTRODUCTION
  • 4. Collaborative Filtering User-based  Neighborhood-basedCollaborative Item-basedFiltering(CF) Bayesian Model Factorization Model  Model-based Maximum Entropy Classification or Clustering ……
  • 5. Motivation To improve the most widely used technology in real-life recommender systems.
  • 6. Neighborhood Model Similarity between users: cov(,)  ◦ Pearson: ∙ ◦ Cosine: ◦ Other similarity measures  Weighted sum of neighbors’ ratings: ◦ , = + ∑∈ , − ∙ , ∑∈ ,Common items:1,4,6Rating vectors of common items: a=[1,4,5] b=[2,2,5]
  • 7. Challenges faced by traditionalmethods Matching similar users (computing similarities ):  Sparsity and noise  Scalability  ……
  • 8. STAR-USER-BASED CF
  • 9. The MPN users Let A, B, C, D are neighbors of users A, B, C, D respectively. Then area E is the set of the most popular neighbors(MPN).
  • 10. What is star user Star users are special users who have rated all items with relatively stable standard. We maintain a small set of star users, and treat them as fixed neighbors of every general user
  • 11. Problem Formulation Filling the following matrix ℛ ∈ × . Items (N) … … ? . . . ?Star users(H) … . . . . . . . , . . ... . . . . . ? . . . ?
  • 12. Prediction Model  Selecting Star Neighbors:  Generate predictions based on star users’ General Users (M) ratings: � , = + ∑∈ , − ∙ , ∑∈ , … … Star Users (H) The parameters are , . . . . . , and , . … . . . . .  . . . . ... . . . . . . . . . . Relationship Matrix W
  • 13. How we get star users(1) 1. Initialization star user matrix ℛ. Training Stage: 2. Predict each rating ̂, in the training set: ∑∈(, − ̅ ) × , ̂, = ̅ + ∑∈ , 3. The residual is , = , − ̂, gradient of , 2 is: and the , 2 = −2, ∙ ∑ −1 ∙, , ∈ ,
  • 14. How we get star users(2) 4. Update each element of matrix ℛ: Training Stage: , , ← , + ∙ , ∙ ∑∈ , 5. Repeat steps 2 to 4 until convergence.
  • 15. How we get star users(3) ◦ (users):The update frequency of ̅ . Parameters: ◦ :The update frequency of , ∈ for each u, and s. w, is computed using Pearson Correlation ∈ × Maintain the relationship matrix W: until recommending stage.
  • 16. EXPERIMENTAL RESULTS
  • 17. Results on MovieLens DatasetRMSE of our approach against Time requirement comparisonvarious H and comparison withkNN
  • 18. Item-based Model We firstly train a small set of star items instead of star users. Predictions are computed as: ∑∈ ′ , − × , � , = ̅ + ∑∈ ′ ,
  • 19. Results on Netflix DatasetOur approach with different values Our approach with different valuesof learning rate of H
  • 20. Discussion Comparison with kNN  Comparison with SVD ◦ Accuracy ◦ Scientific explanation ◦ Data Sparsity ◦ Parameters ◦ Scalability ◦ Updating 2 × ′ → ( × × ′ ) where ≪ .
  • 21. CONCLUSION
  • 22. Summary We proposed a novel CF model based on star users. The original intention is to improve traditional neighborhood-based CF model. Experimental results on two datasets verified the effectiveness of our approach.
  • 23. Future work Incorporating contextual information into our model. Validating our approach in practical applications.
  • 24. THANK YOU

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