  Recommender systems usually face the issue of filter bubbles:
overrecommending homogeneous items based on user features and historical
interactions. Filter bubbles will grow along the feedback loop and
inadvertently narrow user interests. Existing work usually mitigates filter
bubbles by incorporating objectives apart from accuracy such as diversity and
fairness. However, they typically sacrifice accuracy, hurting model fidelity
and user experience. Worse still, users have to passively accept the
recommendation strategy and influence the system in an inefficient manner with
high latency, e.g., keeping providing feedback (e.g., like and dislike) until
the system recognizes the user intention.
  This work proposes a new recommender prototype called UserControllable
Recommender System (UCRS), which enables users to actively control the
mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are
deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands
for users to mitigate the bubbles at different granularities. 3) UCRS can
respond to the controls and adjust the recommendations on the fly. The key to
adjusting lies in blocking the effect of out-of-date user representations on
recommendations, which contains historical information inconsistent with the
control commands. As such, we develop a causality-enhanced User-Controllable
Inference (UCI) framework, which can quickly revise the recommendations based
on user controls in the inference stage and utilize counterfactual inference to
mitigate the effect of out-of-date user representations. Experiments on three
datasets validate that the UCI framework can effectively recommend more desired
items based on user controls, showing promising performance w.r.t. both
accuracy and diversity.
