Personalized Transfer of User Preferences for Cross-domain Recommendation

Yongchun Zhu

Abstract:

Cold-start problem is still a very challenging problem in recommender
systems. Fortunately, the interactions of the cold-start users
in the auxiliary source domain can help cold-start recommendations
in the target domain. How to transfer user’s preferences from
the source domain to the target domain, is the key issue in Crossdomain
Recommendation (CDR) which is a promising solution to
deal with the cold-start problem. Most existing methods model
a common preference bridge to transfer preferences for all users.
Intuitively, since preferences vary from user to user, the preference
bridges of different users should be different. Along this line,
we propose a novel framework named Personalized Transfer of
User Preferences for Cross-domain Recommendation (PTUPCDR).
Specifically, a meta network fed with users’ characteristic embeddings
is learned to generate personalized bridge functions to achieve
personalized transfer of preferences for each user. To learn the meta
network stably, we employ a task-oriented optimization procedure.
With the meta-generated personalized bridge function, the user’s
preference embedding in the source domain can be transformed
into the target domain, and the transformed user preference embedding
can be utilized as the initial embedding for the cold-start user
in the target domain. Using large real-world datasets, we conduct
extensive experiments to evaluate the effectiveness of PTUPCDR on
both cold-start and warm-start stages. The code has been available
at https://github.com/easezyc/WSDM2022-PTUPCDR.