Discrete Factorization Machines for Fast Feature-based Recommendation

Understand

User and item features of side information are crucial for accurate recommendation.

• However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost.
• This prohibits fast recommendation especially on mobile applications where the computational resource is very limited.
• In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.