Relevant topic areas include (but are not limited to):

Search and Ranking. Research on core IR algorithmic topics, such as:

Queries and query analysis.
Web search.
Retrieval models and ranking.
Theoretical models and foundations of information retrieval and access.

System, Efficiency and Scalability. Research on search system aspects that relate to the efficiency of the system and/or its scalability, such as:

Efficient and scalable indexing, crawling, compression, search, and more.
Energy efficiency and green computing for IR.
Search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud, edge IR.

Recommender Systems. Research focusing on recommender systems, rich content representations and content analysis for recommendation, such as:

Filtering and recommendation.
Cross-domain recommendation, socially-aware and context-aware recommender systems, multi-stakeholder recommendations.
Novel approaches to recommendation, including voice, VR/AR, etc.
Other theoretical models and foundations of recommender systems.

Machine Learning for IR. Research bridging ML and IR, such as:

Deep learning for IR.
Reinforcement learning for IR.
Generative IR.
Click models and learning from interactions.
New classification and clustering methods for IR.

Natural Language Processing for IR. Research bridging NLP and IR, such as:

Representation learning for IR.
Large Language Models for IR.
Retrieval Augmented Generation (RAG).
Question Answering.

Conversational or Agentic IR. Research focusing on developing intelligent IR systems that can understand and respond to users' natural language queries and provide relevant information or recommendations through interactive conversations:

End-to-end conversational IR models and optimization.
Session based search or recommendation, user engagement.
Conversational question answer, conversational IR for tasks, dialog systems, spoken language interfaces, intelligent chat systems.
Intelligent personal assistants and agents.

Humans and Interfaces. Research into user-centric aspects of IR including user interfaces, behavior modeling, privacy, interactive systems, such as:

User studies, qualitative and quantitative.
User interfaces and visualization.
Social and collaborative search.
User modeling.

Datasets, Benchmarks, and Evaluations for IR. Research that focuses on the measurement and evaluation of IR systems, such as:

Benchmarks and test collections.
User-centered evaluation.
New methods for building data sets.
Online evaluation.
Session-based evaluation.
Simulation for evaluation.
Metrics.
Evaluation methodology.

Fairness, Accountability, Transparency, Ethics, and Explainability (FATE) in IR. Research on aspects of FATE and bias in search systems and related applications:

Fairness, accountability, transparency and explainability.
Ethics, economics, and politics.

Multi Modal IR. Theoretical, algorithmic or novel practical solutions addressing problems across the domain of multimedia and IR, such as:

Multimedia search and retrieval (e.g., image search, video search, speech and audio search, music search).
Maps and spatial search.

Domain-Specific IR Applications. Research focusing on domain-specific IR challenges, such as:

Local and mobile search.
Social search.
Search in structured data.
Education.
Legal.
Health.
Other applications and domains.

Other IR Topics. Any IR Research that does not fall into any of the areas above. For example, but not limited to:

Explicit semantics.
Information Extraction.
Knowledge acquisition and representation.
Document representation and content analysis.
Information security.