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 email@example.com
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).
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
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
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 ∙, , ∈ ,
How we get star users（2） 4. Update each element of matrix ℛ: Training Stage: , , ← , + ∙ , ∙ ∑∈ , 5. Repeat steps 2 to 4 until convergence.
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.
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.
Future work Incorporating contextual information into our model. Validating our approach in practical applications.