  In the realm of personalized recommendation systems, the increasing concern
is the amplification of belief imbalance and user biases, a phenomenon
primarily attributed to the filter bubble. Addressing this critical issue, we
introduce an innovative intermediate agency (BHEISR) between users and existing
recommendation systems to attenuate the negative repercussions of the filter
bubble effect in extant recommendation systems. The main objective is to strike
a belief balance for users while minimizing the detrimental influence caused by
filter bubbles. The BHEISR model amalgamates principles from nudge theory while
upholding democratic and transparent principles. It harnesses user-specific
category information to stimulate curiosity, even in areas users might
initially deem uninteresting. By progressively stimulating interest in novel
categories, the model encourages users to broaden their belief horizons and
explore the information they typically overlook. Our model is time-sensitive
and operates on a user feedback loop. It utilizes the existing recommendation
algorithm of the model and incorporates user feedback from the prior time
frame. This approach endeavors to transcend the constraints of the filter
bubble, enrich recommendation diversity, and strike a belief balance among
users while also catering to user preferences and system-specific business
requirements. To validate the effectiveness and reliability of the BHEISR
model, we conducted a series of comprehensive experiments with real-world
datasets. These experiments compared the performance of the BHEISR model
against several baseline models using nearly 200 filter bubble-impacted users
as test subjects. Our experimental results conclusively illustrate the superior
performance of the BHEISR model in mitigating filter bubbles and balancing user
perspectives.
