SIGIR-AP is an annual ACM-sponsored conference that promotes and disseminates information retrieval research and development within the Asia-Pacific region defined as all of Asia, Australasia, and the islands of the Pacific and Indian Oceans. It is a the major international forum for the presentation of new research results, and the demonstration of new systems and techniques, in the broad field of information retrieval (IR). The main conference is a two-day event, with tutorial and workshop days before and after the main conference, as required. Other related events, such as CCIR, NTCIR, ADCS, and FIRE may also be scheduled in the days before and after the main conference.

The topics of interest to the SIGIR-AP community include but are not limited to the following areas

• Search and ranking:

     Research on core IR algorithmic topics, including IR at scale, 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 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 data bias).
  • Efficiency and scalability (e.g., indexing, crawling, compression, search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud).
  • Theoretical models and foundations of information retrieval and access (e.g., new theories, fundamental concepts, theoretical analysis).

• Content recommendation, analysis and classification:

     Research focusing on recommender systems, rich content representations and content analysis, such as

  • Filtering and recommendation (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, personalized recommendation).
  • Document representation and content analysis (e.g., summarization, text representation, linguistic analysis, readability, NLP for search, cross-lingual and multilingual search, information extraction, opinion mining and sentiment analysis, clustering, classification, topic models).
  • Knowledge acquisition (e.g. information extraction, relation extraction, event extraction, query understanding, human-in-the-loop knowledge acquisition).

• Machine learning and NLP for search and recommendation:

     Research bridging ML, NLP, and IR, such as

  • Core ML (e.g. deep learning for IR, embeddings, intelligent personal assistants and agents, unbiased learning).
  • Question answering (e.g., factoid and non-factoid question answering, interactive question answering, community-based question answering, question answering systems).
  • Conversational systems (e.g., conversational search interaction, dialog systems, spoken language interfaces, intelligent chat systems).
  • Explicit semantics (e.g. semantic search, named-entities, relation and event extraction).
  • Knowledge representation and reasoning (e.g., link prediction, knowledge graph completion, query understanding, knowledge-guided query and document representation, ontology modeling).

• 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, mixed initiative systems, multi-modal systems).
  • 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 data bias, new experimental approaches, metrics for metrics).

• Fairness, accountability, transparency, ethics, and explainability (FATE) in IR:

     Research on aspects of fairness and information bias in search and recommender systems, such as

  • Fairness, accountability, transparency (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).

• 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).
  • Multimedia search (e.g., image search, video search, speech and audio search, music 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).
  • Knowledge graph applications (e.g. conversational search, semantic search, entity search, KB question answering, knowledge-guided NLP, search and recommendation).
  • Other applications and domains (e.g., digital libraries, enterprise, expert search, news search, app search, archival search, new retrieval problems including applications of search technology for social good).