Relevant areas include:

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

    Queries and query analysis (e.g., query intent, query understanding, query suggestion and prediction, query representation and reformulation, spoken queries)
    Web search (e.g., ranking of web content, ranking at web scale, link analysis, sponsored search, search advertising, adversarial search and spam, vertical search)
    Retrieval models and ranking (e.g., ranking algorithms, learning to rank, language models, retrieval models, combining searches, diversity, aggregated search, dealing with bias)
    Theoretical models and foundations of information retrieval and access (e.g., new theory, fundamental concepts, theoretical analysis)

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 (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, personalized recommendation)
    Cross-domain recommendation, socially- and context-aware recommender systems, multi-stakeholder recommendations
    Data characteristics, data quality, and processing challenges underlying recommender systems
    Novel approaches to recommendation, including voice, VR/AR, etc.
    Preference elicitation, interactive recommender systems
    Other theoretical models and foundations of recommender systems (e.g., economic models)

Machine Learning and Natural Language Processing for IR. Research bridging ML, NLP, and IR.

    Core ML applied to IR, e.g. deep learning for IR, embeddings, reinforcement learning for IR, learning from noisy/few/biased/problematic IR data, generative AI for IR, etc.
    Large Language Models for IR
    Retrieval Augmented Machine Learning
    Question answering (e.g., factoid and non-factoid question answering, interactive question answering, community-based question answering, question answering systems)

Conversational 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
    Modualized IR techniques (e.g., query understanding, user modeling, intent prediction, context and discourse management, reranking and results presentation)
    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:

    Mining and modeling users (e.g., user and task models, click models, log analysis, behavioral analysis, modeling and simulation of information interaction, attention modeling).
    Interactive search (e.g., search interfaces, information access, exploratory search, search context, whole-session support, proactive search, personalized search)
    Social search (e.g., social media search, social tagging, crowdsourcing)
    Collaborative search (e.g., human-in-the-loop, knowledge acquisition)
    Information security (e.g., privacy, surveillance, censorship, encryption, security)
    User studies comparing theory to human behaviour for search and recommendation

Evaluation. Research that focuses on the measurement and evaluation of IR systems, such as:

    User-centered evaluation (e.g., user experience and performance, user engagement, search task design)
    System-centered evaluation (e.g., evaluation metrics, test collections, experimental design, evaluation pipelines, crowdsourcing)
    Beyond Cranfield (e.g., online evaluation, task-based, session-based, multi-turn, interactive search)
    Beyond labels (e.g., simulation, implicit signals, eye-tracking and physiological signals)
    Beyond effectiveness (e.g., value, utility, usefulness, diversity, novelty, urgency, freshness, credibility, authority)
    Methodology (e.g., statistical methods, reproducibility, dealing with bias, new experimental approaches, metrics for metrics)

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

    Fairness, accountability, transparency and explainability (e.g. confidentiality, representativeness, discrimination and harmful bias)
    Ethics, economics, and politics (e.g., studies on broader implications, norms and ethics, economic value, political impact, social good)
    Two-sided search and recommendation scenarios (e.g. matching users and providers, marketplaces)

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

    Multimedia search (e.g., image search, video search, speech and audio search, music search)
    Multimedia recommendation (e.g., image, video, music recommendations)
    Multimodal for IR (e.g., multimodal IR optimization, user intent prediction, multimodal personalization, multimodal for collaborative or exploratory algorithms)

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

    Local and mobile search (e.g., location-based search, mobile usage understanding, mobile result presentation, audio and touch interfaces, geographic search, location context in search)
    Social search (e.g., social networks in search, social media in search, blog and microblog search, forum search)
    Search in structured data (e.g., XML search, graph search, ranking in databases, desktop search, email search, entity-oriented search)
    Education (e.g., search for educational support, peer matching, info seeking in online courses)
    Legal (e.g., e-discovery, patents, other applications in law)
    Health (e.g., medical, genomics, bioinformatics, other applications in health)
    Other applications and domains (e.g., digital libraries, enterprise, expert search, news search, app search, archival search, music search, new retrieval problems including applications of search technology for social good)

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

    Explicit semantics (e.g. semantic search, named-entities, relation and event extraction)
    Knowledge acquisition (e.g. information extraction, relation extraction, event extraction, query understanding, human-in-the-loop knowledge acquisition)
    Knowledge representation and reasoning (e.g., link prediction, knowledge graph completion, query understanding, knowledge-guided query and document representation, ontology modeling)
    Document representation and content analysis (e.g., cross-lingual and multilingual search, summarization, text representation, linguistic analysis, readability, opinion mining and sentiment analysis, clustering, classification, topic models for search and recommendation)

AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.