  Recommender systems mainly tailor personalized recommendations according to
user interests learned from user feedback. However, such recommender systems
passively cater to user interests and even reinforce existing interests in the
feedback loop, leading to problems like filter bubbles and opinion
polarization. To counteract this, proactive recommendation actively steers
users towards developing new interests in a target item or topic by
strategically modulating recommendation sequences. Existing work for proactive
recommendation faces significant hurdles: 1) overlooking the user feedback in
the guidance process; 2) lacking explicit modeling of the guiding objective;
and 3) insufficient flexibility for integration into existing industrial
recommender systems. To address these issues, we introduce an Iterative
Preference Guidance (IPG) framework. IPG performs proactive recommendation in a
flexible post-processing manner by ranking items according to their IPG scores
that consider both interaction probability and guiding value. These scores are
explicitly estimated with iteratively updated user representation that
considers the most recent user interactions. Extensive experiments validate
that IPG can effectively guide user interests toward target interests with a
reasonable trade-off in recommender accuracy. The code is available at
https://github.com/GabyUSTC/IPG-Rec.
