Advances in Flexible Data Representation and Cross-Domain Modeling

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.

Sources

Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation

Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis

Domain Specific Data Distillation and Multi-modal Embedding Generation

Coherence-guided Preference Disentanglement for Cross-domain Recommendations

Modeling and Monitoring of Indoor Populations using Sparse Positioning Data (Extension)

Enhancing CTR Prediction in Recommendation Domain with Search Query Representation

Community search signatures as foundation features for human-centered geospatial modeling

Knowledge Graph Based Visual Search Application

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