Collaborative Recommendation with Multiclass Preference Context

Factorization- and neighborhood-based methods have been recognized as state-of-the-art approaches for collaborative recommendation tasks. In this article, the authors take user ratings as categorical multiclass preferences and propose a novel method called matrix factorization with multiclass preference context (MF-MPC), which integrates an enhanced neighborhood based on the assumption that users with similar past multiclass preferences (instead of one-class preferences in SVD++) will have similar tastes in the future. The main merit of MF-MPC is its ability to make use of the multiclass preference context in the factorization framework in a fine-grained manner and thus inherit the advantages of those two methods. Experimental results on three real-world datasets show that their solution can perform significantly better than factorization-based methods, neighborhood-based methods, and integrated methods with a one-class preference context.

Authors

• Weike Pan
• Zhong Ming

Keywords

• Mathematical model
• Context modeling
• Predictive models
• Collaboration
• Training data
• Data models
• Prediction algorithms
• Collaborative Recommendation
• Model Parameters
• Matrix Factorization
• Latent Space
• Regularization Term
• Usage Rate
• Score Categories
• Recommender Systems
• User Feedback
• Latent Features
• Target User
• Item Pairs
• Prediction Rule
• Explicit Feedback
• Matrix Factorization Method
• Recommendation Algorithm
• True Preferences
• Similar Taste
• Implicit Feedback
• Recommendation Task
• Copies Of The Data
• Collaborative Filtering
• Item Bias
• Neighborhood Information
• Latent Factor Model
• Matrix Factorization Model
• Root Mean Square Error
• Pearson Correlation
• Test Data
• multiclass preference context
• intelligent systems