Quan Bai

Mengyan Wang, Yuxuan Hu, Zihan Yuan et al.

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.

Yungang Yi

Long-context modeling is essential for symbolic music generation, since motif repetition and developmental variation can span thousands of musical events. However, practical composition and performance workflows frequently rely on resource-limited devices (e.g., electronic instruments and portable computers), making heavy memory and attention computation difficult to deploy. We introduce Depth-Structured Music Recurrence (DSMR), a recurrent long-context Transformer for full-piece symbolic music modeling that extends context beyond fixed-length excerpts via segment-level recurrence with detached cross-segment states, featuring a layer-wise memory-horizon schedule that budgets recurrent KV states across depth. DSMR is trained in a single left-to-right pass over each complete composition, akin to how a musician experiences it from beginning to end, while carrying recurrent cross-segment states forward. Within this recurrent framework, we systematically study how depth-wise horizon allocations affect optimization, best-checkpoint perplexity, and efficiency. By allocating different history-window lengths across layers while keeping the total recurrent-state budget fixed, DSMR creates depth-dependent temporal receptive fields within a recurrent attention stack without reducing compute depth. Our main instantiation is a two-scale DSMR schedule that allocates long history windows to lower layers and a uniform short window to the remaining layers. Experiments on the piano performance dataset MAESTRO demonstrate that two-scale DSMR provides a practical quality--efficiency recipe for full-length long-context symbolic music modeling with recurrent attention under limited computational resources.