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19th International Conference on Similarity Search and Applications, SISAP
2026
October 14-16, 2026, Brno, Czech Republic
CORE Rank B conference, https://www.sisap.org/2026/
Topics in brief: Learned Similarity, Embeddings, Vector Databases, Scalable
Retrieval, Multimedia and Multimodality, Similarity Models and Theory, Demos
and Applications
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**Important Dates**
Regular paper deadline: May 29, 2026 (AoE)
Demonstration and Doctoral Symposium paper deadline: June 17, 2026 (AoE)
Special Session proposals: February 27, 2026 (AoE), more info below
SISAP Indexing Challenge: June 17, 2026 (AoE), more info below
**Scope**
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 (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
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**Regular Papers**
Full papers (from 9 to 14 pages in Springer LNCS format) are expected to be
descriptions of complete technical work, whereas short papers (of up to 8
pages) can describe innovative approaches or preliminary results which may
nevertheless 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, and the
reviewers are not expected to take them into account.
**Demonstration Papers**
Demonstration papers (of up to 8 pages in Springer LNCS format) should
provide the motivation for the demonstrated concepts, the information about
the technology and the system to be demonstrated, and should state the
significance of the contribution. The scenarios within which the
demonstrated system applies should also be explained. Evaluation criteria
for the demonstration proposals include: novelty, technical advances and
challenges, and the overall practical attractiveness of the demonstrated
system. A demonstration submission consists of a paper and an additional
1-page appendix (in PDF format) that illustrates how the demo will be
conducted on-site at SISAP. This additional content will not be published in
the conference proceedings, should the submission be accepted.
**Doctoral Symposium Papers**
A submission to the doctoral symposium consists of a paper and an additional
1-page appendix (both in PDF format), which must be single-author and
written by the student alone. The paper should be no longer than 6 pages in
Springer LNCS format (plus up to 2 pages of references). The paper must
describe the problem being addressed, an outline of the planned methodology,
contributions made so far, and 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, should the submission be accepted. This appendix
should describe the benefits that would be obtained by attending the
doctoral symposium, namely the student's motivation to attend SISAP, and
their advisor's word on how the student would benefit by attending the
Doctoral Symposium.
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**Special Session Proposals**
We also welcome special sessions at the conference. Special sessions are
mini-venues that introduce new directions related to the SISAP topics of
interest, but not explicitly listed. Special sessions are typically
organized as a moderated panel, where the authors of special-session papers
will discuss their topic with the panelists. It is expected that special
session chairs and panelists will attend the conference. Special session
papers will supplement the regular research papers and be included in the
LNCS proceedings of SISAP 2026.
Learn more: https://www.sisap.org/2026/call4specialsession.html
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**SISAP Indexing Challenge**
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 and push
the boundaries of efficiency and effectiveness in indexing, filtering, and
searching. The results provide valuable comparisons of competing approaches
and their implementations from given viewpoints and environments. It is
expected that participants prepare a detailed report of their solution and
results in a typical SISAP's short-paper format, which will be included in
the LNCS proceedings of SISAP 2026.
Learn more: https://www.sisap.org/2026/indexingchallenge.html
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Dr. Vasileios Mezaris
Research Director
Head of Intelligent Digital Transformation Laboratory
Information Technologies Institute (ITI)
Centre for Research and Technology Hellas (CERTH)
6th Km Charilaou-Thermi Road
P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece
Tel: +30 2311 257770, email: bmezaris@iti.gr
web: <http://www.iti.gr/~bmezaris> http://www.iti.gr/~bmezaris
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