  User behavior has been validated to be effective in revealing personalized
preferences for commercial recommendations. However, few user-item interactions
can be collected for new users, which results in a null space for their
interests, i.e., the cold-start dilemma. In this paper, a two-tower framework,
namely, the model-agnostic interest learning (MAIL) framework, is proposed to
address the cold-start recommendation (CSR) problem for recommender systems. In
MAIL, one unique tower is constructed to tackle the CSR from a zero-shot view,
and the other tower focuses on the general ranking task. Specifically, the
zero-shot tower first performs cross-modal reconstruction with dual
auto-encoders to obtain virtual behavior data from highly aligned hidden
features for new users; and the ranking tower can then output recommendations
for users based on the completed data by the zero-shot tower. Practically, the
ranking tower in MAIL is model-agnostic and can be implemented with any
embedding-based deep models. Based on the co-training of the two towers, the
MAIL presents an end-to-end method for recommender systems that shows an
incremental performance improvement. The proposed method has been successfully
deployed on the live recommendation system of NetEase Cloud Music to achieve a
click-through rate improvement of 13% to 15% for millions of users. Offline
experiments on real-world datasets also show its superior performance in CSR.
Our code is available.
