The International Conference on Similarity Search and Applications (SISAP)
focuses on research in similarity-based data management and retrieval, with
emphasis on embedding-based methods, vector databases, and
machine-learning-driven similarity search.

SISAP covers similarity models, indexing and query processing, scalable and
distributed similarity systems, learned and adaptive techniques, and
similarity-aware database architectures supporting high-dimensional and
multimodal data.

Originating from metric indexing research, SISAP is the only international
conference dedicated exclusively to similarity search, spanning theory,
systems, evaluation, and applications across data management, information
retrieval, and machine learning.


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, including:
- k-NN
- range queries
- reverse nearest-neighbor queries
- top-k queries
- 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


Regular Papers
==============
- Full papers:
- 9 to 14 pages in Springer LNCS format
- Expected to describe complete technical work

- Short papers:
- Up to 8 pages in Springer LNCS format
- Can describe innovative approaches or preliminary results that may require
more work to mature

- Vision papers and other position papers:
- Should be submitted as short research papers

- Page limits:
- Include references
- Any appendices, if needed, can only be posted online
- Reviewers are not expected to take appendices into account


Demonstration Papers
====================
Demonstration papers should:
- Be up to 8 pages in Springer LNCS format
- Provide the motivation for the demonstrated concepts
- Describe the technology and system to be demonstrated
- State the significance of the contribution
- Explain the scenarios in which the demonstrated system applies

Evaluation criteria for demonstration proposals include:
- Novelty
- Technical advances and challenges
- Overall practical attractiveness of the demonstrated system

A demonstration submission consists of:
- A paper
- An additional 1-page appendix in PDF format

The appendix should illustrate how the demo will be conducted on-site at SISAP.
This additional content will not be published in the conference proceedings if
the submission is accepted.


Doctoral Symposium Papers
=========================
A submission to the doctoral symposium consists of:
- A paper
- An additional 1-page appendix

Both must be submitted in PDF format. The submission must be single-author and
written by the student alone.

The paper should:
- Be no longer than 6 pages in Springer LNCS format
- Include up to 2 additional pages of references
- Describe the problem being addressed
- Outline the planned methodology
- Describe contributions made so far
- Describe the work lying ahead as part of the author’s PhD study

The additional 1-page appendix:
- Will not be published in the conference proceedings if the submission is accepted
- Should describe the benefits of attending the doctoral symposium
- Should include:
- The student’s motivation to attend SISAP
- The advisor’s statement on how the student would benefit from attending the
Doctoral Symposium


SISAP Indexing Challenge
========================
The SISAP Indexing Challenge is an event for researchers and practitioners aimed
at advancing the state of the art in large-scale similarity data management.

The challenge provides a platform to:
- Showcase innovative solutions
- Push the boundaries of efficiency and effectiveness in indexing, filtering,
and searching
- Provide valuable comparisons of competing approaches and their implementations
from given viewpoints and environments

Participants are expected to prepare a detailed report of their solution and
results in a typical SISAP short-paper format. Accepted reports will be included
in the LNCS proceedings of SISAP 2026.