14th Italian Information Retrieval Workshop

IIR offers the opportunity to present and discuss both theoretical and empirical research. Relevant topics include, but are not restricted to:

• Search and Ranking. Research on core Information Retrieval (IR), including IR at scale, covering topics such as:
  • Theoretical models and foundations of IR and access
  • Retrieval and ranking models, including diversity and aggregated search
  • Web search, including link analysis, sponsored search, search advertising, adversarial search and spam, and vertical search
  • Cross- and multi-lingual search
  • Queries and query analysis

• Recommendation. Research focusing on Information Filtering and Recommender Systems, covering topics such as:
  • Information Filtering and Recommendation, including Collaborative Filtering, Content-based Filtering, Hybrid Recommender Systems, Context-aware Recommendation
  • Deep Learning in RecSys, Scalability and Efficiency, Evaluation Metrics and Methodologies, User Interfaces and Visualization
  • Privacy, Security, and Ethics, Domain-specific Recommendations, Social and Trust-based Recommendations, Explainable and Interpretable Recommendations

• Content Representation and Analysis. Research focusing on rich content representations and analysis, covering topics such as:
  • Document representation, including multimodal representation
  • Content analysis and information extraction, including readability, sentiment analysis, and opinion mining
  • Clustering, classification, summarization, and topic modeling

• Artificial Intelligence, NLP, Semantics, and Dialog. Research bridging AI and IR –, especially toward deep semantics — and dialog with intelligent agents, covering topics such as:
  • Question Answering
  • Conversational systems, including spoken language interfaces, dialog management systems, and intelligent chat systems
  • Semantics and knowledge graphs
  • Deep learning for IR, embeddings, Large Language Models, and agents

• Domain-Specific Applications. Research focusing on domain-specific challenges, covering topics such as:
  • Social search
  • Search in structured data, including email and entity search
  • Multimedia search
  • Search and recommendation for Educational, Legal, Health – including genomics and bioinformatics – and Academic domains
  • Other domains such as digital libraries, enterprise, news, app, and archival search

• Human Factors and Interfaces. Research into user-centric aspects of IR, including user interfaces, behavior modeling, privacy, and interactive systems, covering topics such as:
  • Mining and modeling search activity, including user and task models, click models, log analysis, behavioral analysis, and attention modeling
  • Interactive and personalized search and recommendation
  • Collaborative search, social tagging, and crowdsourcing
  • Information privacy and security

• Evaluation. Research that focuses on the measurement and evaluation of IR and Recommender Systems, covering topics such as:
  • User-centered evaluation methods, including measures of user experience and performance, user engagement, and search task design
  • Test collections and evaluation metrics, including the development of new test collections
  • Evaluation of novel information access tasks and systems such as multi-turn information access
  • Statistical methods and reproducibility issues in information retrieval evaluation
  • Efficiency and scalability

• Future Directions. Research with theoretical or empirical contributions on new technical or social aspects of IR, especially in more speculative directions or with emerging technologies, covering topics such as::
  • Novel approaches to IR and RS
  • Ethics, economics, and politics
  • Applications of search and recommendation to social good
  • IR and RS with new devices, including wearable computing, neuroinformatics, sensors, Internet-of-Things, vehicles