Preference-Adaptive Meta-Learning for Cold-Start Recommendation

Li Wang, Binbin Jin, Zhenya Huang, Hongke Zhao, Defu Lian, Qi Liu, Enhong Chen

Abstract:

In recommender systems, the cold-start problem is
a critical issue. To alleviate this problem, an emerging direction adopts meta-learning frameworks and
achieves success. Most existing works aim to learn
globally shared prior knowledge across all users so
that it can be quickly adapted to a new user with
sparse interactions. However, globally shared prior
knowledge may be inadequate to discern users’
complicated behaviors and causes poor generalization. Therefore, we argue that prior knowledge
should be locally shared by users with similar preferences who can be recognized by social relations.
To this end, in this paper, we propose a PreferenceAdaptive Meta-Learning approach (PAML) to improve existing meta-learning frameworks with better generalization capacity. Specifically, to address
two challenges imposed by social relations, we first
identify reliable implicit friends to strengthen a
user’s social relations based on our defined palindrome paths. Then, a coarse-fine preference modeling method is proposed to leverage social relations
and capture the preference. Afterwards, a novel
preference-specific adapter is designed to adapt the
globally shared prior knowledge to the preferencespecific knowledge so that users who have similar
tastes share similar knowledge. We conduct extensive experiments on two publicly available datasets.
Experimental results validate the power of social
relations and the effectiveness of PAML.