  Traditional recommender systems are typically passive in that they try to
adapt their recommendations to the user's historical interests. However, it is
highly desirable for commercial applications, such as e-commerce, advertisement
placement, and news portals, to be able to expand the users' interests so that
they would accept items that they were not originally aware of or interested in
to increase customer interactions. In this paper, we present Influential
Recommender System (IRS), a new recommendation paradigm that aims to
proactively lead a user to like a given objective item by progressively
recommending to the user a sequence of carefully selected items (called an
influence path). We propose the Influential Recommender Network (IRN), which is
a Transformer-based sequential model to encode the items' sequential
dependencies. Since different people react to external influences differently,
we introduce the Personalized Impressionability Mask (PIM) to model how
receptive a user is to external influence to generate the most effective
influence path for the user. To evaluate IRN, we design several performance
metrics to measure whether or not the influence path can smoothly expand the
user interest to include the objective item while maintaining the user's
satisfaction with the recommendation. Experimental results show that IRN
significantly outperforms the baseline recommenders and demonstrates its
capability of influencing users' interests.
