  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. Hence, in this work,
we propose a framework that turns massive user behavior data to retrievable
knowledge (D2K). It is a data-centric approach that is model-agnostic and easy
to scale up. Different from only storing unary knowledge such as the user-side
or item-side information, D2K propose to store ternary knowledge for
recommendation, which is determined by the complete recommendation factors --
user, item, and context. The knowledge retrieved by target samples can be
directly used to enhance the performance of any recommendation algorithms.
Specifically, we introduce a Transformer-based knowledge encoder to transform
the old data into knowledge with the user-item-context cross features. A
personalized knowledge adaptation unit is devised to effectively exploit the
information from the knowledge base by adapting the retrieved knowledge to the
target samples. Extensive experiments on two public datasets show that D2K
significantly outperforms existing baselines and is compatible with a major
collection of recommendation algorithms.
