The hyperspherical quantization approach, while effective, may have potential limitations when faced with challenging real-world scenarios. One limitation could be the scalability of the hypersphere model when dealing with a large number of tags or high-dimensional data. To address this, techniques like dimensionality reduction or advanced clustering methods can be employed to manage the complexity of the data. Additionally, the hyperspherical quantization approach may struggle with handling noisy or ambiguous tags, leading to decreased quantization performance. To improve in such scenarios, incorporating robust tag filtering mechanisms or utilizing attention mechanisms to focus on relevant tags can enhance the model's performance in challenging real-world settings.