D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems

Understand

A vast amount of user behavior data is constantly accumulating on today's large recommendation platforms, recording users' various interests and tastes.

• Preserving knowledge from the old data while new data continually arrives is a vital problem for recommender systems.
• Existing approaches generally seek to save the knowledge implicitly in the model parameters.
• However, such a parameter-centric approach lacks scalability and flexibility -- the capacity is hard to scale, and the knowledge is inflexible to utilize.