The recent developments in the research area of recommendation systems and data modeling have shown a significant shift towards more flexible and efficient representation techniques. Researchers are increasingly focusing on ID-free item representation, multidimensional knowledge graph embeddings, and domain-specific data distillation to enhance the performance and applicability of models. Notably, the integration of multimodal data and the use of learnable tokens are emerging as key strategies to overcome the limitations of traditional ID-based models, particularly in sparse data environments. Additionally, the exploration of cross-domain recommendations and the use of coherence-guided preference disentanglement are advancing the field by improving the accuracy of user preference predictions across different platforms. Furthermore, the application of probabilistic modeling and learning-based estimators for indoor population monitoring demonstrates a novel approach to handling sparse data in real-world scenarios. The incorporation of search query representation in click-through rate prediction and the use of community search signatures for geospatial modeling highlight the interdisciplinary potential of these advancements. Overall, these innovations are paving the way for more robust, scalable, and contextually aware recommendation and data modeling systems.