Semantic Web Technology and Recommender Systems 2nd Edition

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 6634

Special Issue Editors


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Guest Editor
Department of Cultural Technology and Communication, School of Social Sciences, 81100 Mytilene, Greece
Interests: IoT; ontologies and semantic web
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Management Science and Technology, University of the Peloponnese, 22131 Tripoli, Greece
Interests: recommender systems; usability; social media analysis; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the Special Issue of Big Data and Cognitive Computing on “Semantic Web Technology and Recommender Systems”, we are delighted to announce a new Special Issue entitled “Semantic Web Technology and Recommender Systems 2nd Edition”.

Semantic web technologies define and analyse web data, linked or not, to enable semantic interconnection. This allows data analysts, application designers and cross-domain experts (linguists, cognitive scientists, machine learning experts, user interface designers) to utilise data semantics to build and work on approaches and ideas that require a deep understanding of the data at hand. Data-driven methods in computation and especially in recommender systems analyse single-source big data to identify and select recommendable content for users and applications. Multi-source data are a larger challenge. Such data are of immense value to understanding the user expectations and redefining the goals for content recommendation. The challenge is that combining data from distinct sources and for an undefined or unknown original target has to go through a layer of data understanding. Advanced data management and knowledge graphs are potential means of achieving the interlinking of data from original, social, cognitive and world sources.

This Special Issue will present the state-of-the-art in:

  • semantic web methods and tools for advanced data analysis
  • design and development of social data-driven applications
  • intelligent analysis of complex data
  • linguistic and psychological analysis of data
  • human factors and the semantics of language communication
  • methods for the enrichment of recommendation systems
  • deep learning techniques for identifying and recommending content
  • models, tools and methods that assist or supplement recommender systems
  • privacy and security for semantic data management
  • big data analytics for recommendation systems
  • analytics and recommendation systems for semantic trajectories
  • semantic sentiment analysis of big social data
  • social and semantic web applications to politics
  • social and semantic web applications to terrorism
  • social and semantic web applications to psychology
  • social and semantic web applications to societal issues
  • research results in digital marketing and technologies for the common good
  • user studies on semantic web and recommender systems

Dr. Konstantinos Kotis
Dr. Dimitris Spiliotopoulos
Guest Editors

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. Big Data and Cognitive Computing 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 1800 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

  • semantic web
  • semantics
  • recommender system
  • recommendation method
  • big data analytics
  • sentiment analysis
  • social web

Published Papers (2 papers)

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Research

13 pages, 398 KiB  
Article
Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services
by Luong Vuong Nguyen, Quoc-Trinh Vo and Tri-Hai Nguyen
Big Data Cogn. Comput. 2023, 7(2), 106; https://doi.org/10.3390/bdcc7020106 - 31 May 2023
Cited by 8 | Viewed by 4800
Abstract
In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite [...] Read more.
In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite its popularity, collaborative filtering has limitations that researchers aim to overcome. In this paper, we enhance the K-nearest neighbor (KNN)-based collaborative filtering algorithm for a recommendation system, by considering the similarity of user cognition. This enhancement aimed to improve the accuracy in grouping users and generating more relevant recommendations for the active user. The experimental results showed that the proposed model outperformed benchmark models, in terms of MAE, RMSE, MAP, and NDCG metrics. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems 2nd Edition)
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20 pages, 2519 KiB  
Article
Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets
by Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos and Stefanos Ougiaroglou
Big Data Cogn. Comput. 2023, 7(2), 59; https://doi.org/10.3390/bdcc7020059 - 24 Mar 2023
Viewed by 1343
Abstract
Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are [...] Read more.
Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered “close” to the user for whom the prediction is being generated have given to the same product or service. In general, “close” users for some user u correspond to users that have rated items similarly to u and these users are termed as “near neighbors”. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset’s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method’s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems 2nd Edition)
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