Information Retrieval, Recommender Systems and Adaptive Systems

Dear Colleagues,

The growing amount of data produced daily has made their exploration impossible without using search and filtering tools and strategies. Indeed, the end user often spends hours searching on the Web for elements compliant with their needs, thereby facing a complex and time-consuming task. This scenario underlines the ever more current need to create systems to support the end user in the search, filtering, and consumption of such huge amounts of information. In this regard, adaptive and personalized systems play an increasingly important role in our daily lives as we more and more rely on systems that adapt their behavior based on our preferences and needs and support us in a wide range of heterogeneous decision-making tasks.

In such regards, Machine Learning approaches have been recently demonstrated effective to process heterogeneous information providing novel solution not only for the task of content analysis, but also for their retrieval and recommendation. The evolution of such approaches has made it possible to discover new threats, limits, and challenges that needs our attention. The cost of performances, the development of strategies to correctly evaluate those algorithms, and how to exploit the context of usage and psychological and emotional reactions are only few of the possible new research trends.

This Special Issue on information retrieval, recommender systems, and adaptive systems is aimed at industrial and academic researchers who apply non-traditional methods to tasks related with the management of huge amounts of information. The key areas of this Special Issue include, but are not limited to:

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 bias)
• Efficiency and scalability (e.g., indexing, crawling, compression, search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud)

Foundations and theory of IR. Research with theoretical or empirical contributions on technical or social aspects of IR, such as:

• Theoretical models and foundations of information retrieval and access (e.g., new theories, fundamental concepts, theoretical analysis)
• Ethics, economics, and politics (e.g., studies on broader implications, norms and ethics, economic value, political impact, social good)
• Fairness, accountability, transparency (e.g., confidentiality, representativeness, discrimination, and harmful bias)

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)

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)

Artificial Intelligence, semantics, and dialog. Research bridging AI and IR, especially toward deep semantics and dialog with intelligent agents, such as:

• Core AI (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)

Human factors and interfaces. Research on 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)

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).
• 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)

Dr. Marco Polignano
Guest Editor

Prof. Dr. Giovanni Semeraro
Co-Guest Editor

Manuscript Submission Information

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• Search and ranking
• Foundations and theory of IR
• Domain-specific applications
• Content recommendation, analysis, and classification
• Artificial Intelligence, semantics, and dialog
• Human factors and interfaces
• Evaluation