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