New Trends in Algorithms for Intelligent Recommendation Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 18595

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Oviedo, 33007 Oviedo, Spain
Interests: web engineering; artificial Intelligence; recommendation systems; health informatics; modeling software with DSL and MDE
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, University of Oviedo, 33007 Oviedo, Spain
Interests: domain-specific languages; model-driven engineering; business process management; machine learning; Internet of Things and e-learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the problem of information overload is more present than ever due to the rapid development of the Internet and the Web. As a consequence of the large number of electronic resources available on the Internet, it is becoming increasingly complex to select the most relevant and significant information for users.

To solve the problem of information overload, various techniques, algorithms and tools are currently used to analyze, classify or filter this huge amount of data with the aim of analyzing the users’ behavior, interests or tastes. Among these tools are machine learning, big data, natural language processing or recommender systems.

A recommender system is a set of information retrieval techniques that, through advanced analysis of massive data, can select the most relevant and significant information for users in order to help them to make intelligent decisions. These systems use different kinds of algorithms to help users to discover quickly and easily the information that they need in a specific context through information filtering.

With the development and implementation of efficient algorithms for recommender systems, users can find different types of information such as hotels, movies, series, books, songs, websites, electronic products, games, tourist points of interest, toys and any kind of information that may interest them.

Among the most popular implementation algorithms of these systems, we can highlight content-based, collaborative filtering and hybrid recommender systems, among others. In order to make good predictions, these systems use the collective ratings made by users of a set of data, which are obtained explicitly or implicitly.

As recommender systems take on an increasingly central role in decision making in different scenarios, the need for researchers and developers to be able to refine and propose new models, algorithms to convert unstructured data into structured data or algorithms to optimize the performance of these systems become more important, considering that these algorithms are usually adapted to the set of data available for a particular domain of knowledge.

This Special Issue on “New Trends in Algorithms for Intelligent Recommendation Systems” provides a platform to exchange new ideas by researchers and practitioners in the field of recommender systems and their applications in many areas.

We encourage authors across the world to submit their original and unpublished works. We have a special interest in works focusing on the topics listed below, but we are open to other works that fit the theme of the Special Issue.

  • Recommender systems algorithms
  • Collaborative filtering algorithms
  • Content-based filtering algorithms
  • Algorithms for hybrid recommender systems
  • Algorithms for demographic recommender systems
  • Algorithms for recommender systems based on deep learning
  • Explainable AI algorithms for recommender systems
  • Implicit and explicit feedback algorithms
  • Privacy and security in recommender systems
  • Machine learning algorithms applied to recommender systems
  • Active learning for recommender systems
  • Legal and ethical issues in recommender systems

Dr. Edward Rolando Núñez-Valdez
Dr. Vicente García-Díaz
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. Algorithms 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 1600 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.

Published Papers (7 papers)

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Research

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18 pages, 424 KiB  
Article
Information Retrieval and Machine Learning Methods for Academic Expert Finding
by Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños
Algorithms 2024, 17(2), 51; https://doi.org/10.3390/a17020051 - 23 Jan 2024
Viewed by 1759
Abstract
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential [...] Read more.
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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18 pages, 1378 KiB  
Article
Using Graph Neural Networks for Social Recommendations
by Dharahas Tallapally, John Wang, Katerina Potika and Magdalini Eirinaki
Algorithms 2023, 16(11), 515; https://doi.org/10.3390/a16110515 - 10 Nov 2023
Viewed by 2062
Abstract
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the [...] Read more.
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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16 pages, 722 KiB  
Article
An Efficient Approach to Manage Natural Noises in Recommender Systems
by Chenhong Luo, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang and Leo Yu Zhang
Algorithms 2023, 16(5), 228; https://doi.org/10.3390/a16050228 - 27 Apr 2023
Cited by 1 | Viewed by 1447
Abstract
Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates [...] Read more.
Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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26 pages, 4411 KiB  
Article
A Novel Hybrid Recommender System for the Tourism Domain
by Georgios Chalkiadakis, Ioannis Ziogas, Michail Koutsmanis, Errikos Streviniotis, Costas Panagiotakis and Harris Papadakis
Algorithms 2023, 16(4), 215; https://doi.org/10.3390/a16040215 - 21 Apr 2023
Cited by 7 | Viewed by 2361
Abstract
In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a [...] Read more.
In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a “winner”: the most preferable variant of our hybrid recommender algorithm is one using a ⟨four elicitation slates, six shown images per slate⟩ pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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17 pages, 3571 KiB  
Article
Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
by Antiopi Panteli and Basilis Boutsinas
Algorithms 2023, 16(4), 182; https://doi.org/10.3390/a16040182 - 27 Mar 2023
Cited by 7 | Viewed by 4711
Abstract
Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most [...] Read more.
Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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Review

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28 pages, 359 KiB  
Review
A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
by Christie I. Ezeife and Hemni Karlapalepu
Algorithms 2023, 16(10), 467; https://doi.org/10.3390/a16100467 - 3 Oct 2023
Viewed by 1833
Abstract
E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases [...] Read more.
E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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Other

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16 pages, 235 KiB  
Perspective
Sparks of Artificial General Recommender (AGR): Experiments with ChatGPT
by Guo Lin and Yongfeng Zhang
Algorithms 2023, 16(9), 432; https://doi.org/10.3390/a16090432 - 8 Sep 2023
Cited by 2 | Viewed by 1259
Abstract
This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental [...] Read more.
This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental principles that an AGR should adhere to, each with its corresponding testing protocol. We proceed to assess whether ChatGPT, a sophisticated LLM, can comply with the proposed principles by engaging in recommendation-oriented dialogues with the model while observing its behavior. Our findings demonstrate the potential for ChatGPT to serve as an AGR, though several limitations and areas for improvement are identified. Full article
(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)
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