Topics of Interest
The SISAP conference solicits original research contributions on similarity search and its applications. Topics of interest include, but are not limited to:
- Similarity Models and Theory
- Models of similarity and dissimilarity in metric and non-metric spaces
- Intrinsic dimensionality, concentration phenomena, hubness, and discriminability
- Manifolds, embeddings, and geometric properties of similarity spaces
- Theoretical foundations and limits of similarity search and indexing
- Learning and Representations
- Feature extraction and representation learning for similarity search
- Metric learning and learned similarity measures
- Embeddings from self-supervised and foundation models
- Multimodal and cross-modal similarity representations
- Similarity Queries and Processing
- Similarity queries and operators (k-NN, range, reverse NN, top-k, diversity queries)
- Exact, approximate, and probabilistic similarity search
- Similarity joins, ranking, filtering, and aggregation
- Query semantics and languages for similarity-based data
- Cross-modal similarity search
- Indexing and Scalable Systems
- Indexing and access methods for similarity search
- Graph-based, tree-based, hashing, quantization, and hybrid approaches
- Learned and adaptive index structures
- Parallel, distributed, and GPU-accelerated similarity processing
- Dynamic, streaming, and update-aware similarity systems
- Similarity-Aware Data Management
- Similarity search in database and data management systems
- Vector databases and similarity-native storage engines
- Query optimization and execution for similarity workloads
- Integration of similarity search with relational, graph, and hybrid systems
- Cloud-native and large-scale similarity services
- Evaluation and Benchmarks
- Evaluation methodologies and cost models for similarity processing
- Benchmark datasets, workloads, and experimental frameworks
- Accuracy–efficiency trade-offs and reproducibility
- Applications
- Similarity search in multimedia, scientific, industrial, and emerging data domains
- Similarity search in healthcare, sports, robotics, security, and other fields
- Dense retrieval and semantic search
- Recommendation systems and personalization
- Search and question-answering within content collections
