Dear Colleagues,

The constant increase in the amount of data and information available on the web has resulted in the development of systems that can support users in determining increasingly important relevant decisions. Recommender systems (RSs), information retrieval (IR) and adaptive systems (ASs) have emerged as effective strategies for overcoming information overload. The utility of these systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. Although in early works about this topic there was a strong interest in ways to make such systems proactive, user-friendly, and persuasive, over time, they became increasingly focused solely on the algorithmic component. However, this trend is gradually being reversed and always more attention is currently placed on human decision-making models that focus on supporting the end user in understanding what is being proposed through the RSs by using dynamic and persuasive interfaces. Moreover, the past few decades have witnessed the tremendous success of deep learning in many application domains, such as natural language processing. The academia and industry have been in a race to apply deep learning to a wider range of applications due to its capability in solving many complex tasks while providing start-of-the-art results. The evolution of such approaches has allowed for it to be possible to discover novel threats, limits, and challenges that need our attention. The cost of the 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 a few of the possible novel research trends.

This Special Issue on recommender systems, information retrieval, and adaptive systems is aimed towards industrial and academic researchers who apply nontraditional 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:

Recommendation, analysis, and classification

• Filtering and recommendation (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, and 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, and topic models);
• Knowledge acquisition (e.g., information extraction, relation extraction, event extraction, query understanding, and human-in-the-loop knowledge acquisition);
• User modeling and personalization (e.g., adaptive hypermedia, semantic, social web, and personalization for persuasive and behavior change systems).

Search and ranking

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

Foundations and Theory of Information Retrieval and Information Filtering

• Theoretical models and foundations of information retrieval and filtering (e.g., novel 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

• Mobile services (e.g., location-based search and recommendation, mobile usage understanding, mobile result presentation, audio and touch interfaces, geographic search, and location context in search);
• Social media (e.g., social networks and media analysis and search, blog and microblog search and recommendation, etc.);
• Structured data (e.g., XML, graph, ranking in databases, etc.);
• Multimedia search (e.g., image search, video search, speech and audio search, and music search);
• Education (e.g., educational support, peer matching, and info seeking in online courses).
• Legal (e.g., e-discovery, patents, and other applications in law);
• Health (e.g., medical, genomics, bioinformatics, and other applications in health)
• Knowledge graph applications (e.g., conversational models, semantic search, entity search, KB question answering, and knowledge-guided NLP search and recommendation);
• Quantum computing (e.g., quantum information retrieval, quantum recommender systems, and quantum deep and machine learning approaches for IR and RSs);
• Other applications and domains (e.g., digital libraries, enterprise, expert search, news recommendations, app search, archival search, and novel retrieval and filtering problems, including applications of search technology for social good).

Artificial Intelligence, semantics, and dialog

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

Human factors and interfaces

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

Evaluation

• User-centered evaluation (e.g., user experience and performance, user engagement, and search task design);
• System-centered evaluation (e.g., evaluation metrics, test collections, and experimental design);
• Beyond labels (e.g., simulation, implicit signals, and eye-tracking and physiological signals);
• Beyond effectiveness (e.g., value, utility, usefulness, diversity, novelty, urgency, freshness, credibility, and authority);
• Methodology (e.g., statistical methods, reproducibility, dealing with bias, and novel experimental approaches).

Dr. Marco Polignano
Prof. Dr. Giovanni Semeraro
Guest Editors

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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