Special Issue "Information Retrieval, Recommender Systems and Adaptive Systems"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Marco Polignano
E-Mail Website
Guest Editor
Department of Computer Science, University of Bari Aldo Moro, 70121 Bari, Italy
Interests: recommender systems; information retrieval; emotion detection and elaborations; natural language processing; artificial intelligence; machine learning; user profiling
Prof. Dr. Giovanni Semeraro
E-Mail Website
Co-Guest Editor
Department of Computer Science, University of Bari Aldo Moro, 70121 Bari, Italy
Interests: recommender systems; information retrieval; user modeling; AI & machine learning; semantic & social technologies

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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

Published Papers (2 papers)

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Research

Article
A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization
Information 2021, 12(8), 296; https://doi.org/10.3390/info12080296 - 26 Jul 2021
Viewed by 107
Abstract
Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. [...] Read more.
Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
An Academic Text Recommendation Method Based on Graph Neural Network
Information 2021, 12(4), 172; https://doi.org/10.3390/info12040172 - 16 Apr 2021
Viewed by 474
Abstract
Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, [...] Read more.
Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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