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: closed (30 April 2022) | Viewed by 7883

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 submissions that pass pre-check are 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 (9 papers)

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Research

Communication
Integrating Human Factors in the Visualisation of Usable Transparency for Dynamic Risk Assessment
Information 2022, 13(7), 340; https://doi.org/10.3390/info13070340 - 14 Jul 2022
Viewed by 369
Abstract
Modern technology and the digitisation era accelerated the pace of data generation and collection for various purposes. The orchestration of such data is a daily challenge faced by even experienced professional users in the context of Internet of Things (IoT)-enabled environments, especially when [...] Read more.
Modern technology and the digitisation era accelerated the pace of data generation and collection for various purposes. The orchestration of such data is a daily challenge faced by even experienced professional users in the context of Internet of Things (IoT)-enabled environments, especially when it comes to cybersecurity and privacy risks. This article presents the application of a user-centric process for the visualisation of automated decision making security interventions. The user interface (UI) development was guided by iterative feedback collection from user studies on the visualisation of a dynamic risk assessment (DRA)-based security solution for regular lay users. The methodology we applied starts with the definition of the methodological process to map possible technical actions to related usable actions. The definition and refinement of the user interface (UI) was controlled by the survey feedback loop from end user studies on their general technological knowledge, experience with smart homes, cybersecurity awareness and privacy preservation needs. We continuously improved the visualisation interfaces for configuring a cybersecurity solution and adjusting usable transparency of the control and monitoring of the dynamic risk assessment (DRA). For this purpose, we have designed, developed and validated a decision tree workflow and showed the evolution of the interfaces through various stages of the real-life trials executed under European H2020 project GHOST. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets
Information 2022, 13(6), 302; https://doi.org/10.3390/info13060302 - 15 Jun 2022
Viewed by 526
Abstract
The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms [...] Read more.
The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system’s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
An Approach to Churn Prediction for Cloud Services Recommendation and User Retention
Information 2022, 13(5), 227; https://doi.org/10.3390/info13050227 - 28 Apr 2022
Viewed by 728
Abstract
The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service [...] Read more.
The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
Recommendation System Algorithms on Location-Based Social Networks: Comparative Study
Information 2022, 13(4), 188; https://doi.org/10.3390/info13040188 - 08 Apr 2022
Viewed by 806
Abstract
Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has [...] Read more.
Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has gained considerable attention in research using techniques and methods based on users’ geosocial activities. In this study, we present a comparative analysis of three matrix factorization (MF) algorithms, namely, singular value decomposition (SVD), singular value decomposition plus (SVD++), and nonnegative matrix factorization (NMF). The primary task of the implemented recommender system was to predict restaurant ratings for each user and make a recommendation based on this prediction. This experiment used two performance metrics for evaluation, namely, root mean square error (RMSE) and mean absolute error (MAE). The RMSEs confirmed the efficacy of SVD with a lower error rate, whereas SVD++ had a lower error rate in terms of MAE. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm
Information 2022, 13(2), 70; https://doi.org/10.3390/info13020070 - 29 Jan 2022
Cited by 1 | Viewed by 900
Abstract
Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to [...] Read more.
Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client’s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients’ preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison
Information 2022, 13(1), 42; https://doi.org/10.3390/info13010042 - 17 Jan 2022
Cited by 1 | Viewed by 879
Abstract
Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating [...] Read more.
Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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Article
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images
Information 2021, 12(8), 321; https://doi.org/10.3390/info12080321 - 11 Aug 2021
Viewed by 657
Abstract
Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has [...] Read more.
Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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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
Cited by 1 | Viewed by 825
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 812
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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