Recommender Systems: Approaches, Challenges and Applications (Volume II)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13657

Special Issue Editors


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Guest Editor
GECAD, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Interests: artificial intelligence; group decision support systems; argumentation-based dialogues; affective computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
GECAD, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Interests: recommender systems; group recommender systems; affective computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
GECAD, Institute of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: artificial intelligence; multiagent systems; emotional agents; persuasive argumentation; group decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recommender systems have been applied in several domains (e.g., tourism, health, education, e-commerce, etc.) to help users determine more satisfactory choices. The possibility of formulating personalized recommendations enhances the effectiveness of recommender systems. As such, considering aspects such as user preferences, personality and expectations can improve the quality of recommendations. It is important to study and develop new intelligent strategies allowing a greater awareness of the user or group of users, while considering new ways of evaluating recommendation systems, such as diversity, satisfaction, user experience, coverage, trust, fairness, and transparency.

The purpose of this Special Issue is to explore novel artificial intelligence solutions for overcoming the current challenges of recommender systems and to improve the quality of recommendations.

Topics relevant for this Special Issue include:

  • Group recommender systems;
  • Cross-domain recommendations;
  • Context-aware recommender systems;
  • Personalized recommendations;
  • Recommendations based on machine learning/deep learning;
  • Novelty, diversity or serendipity in recommender systems;
  • Explanation methods for recommender systems;
  • Cognitive and affective aspects in recommender systems (emotions, personality, mood, motivations, etc.);
  • Transfer learning in recommender system.

Dr. João Carneiro
Dr. Patrícia Alves
Dr. Goreti Marreiros
Guest Editors

Manuscript Submission Information

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Keywords

  • recommender systems
  • group recommender systems
  • cold-start problem
  • collaborative filtering
  • content-based filtering
  • hybrid recommender systems
  • machine learning
  • deep learning
  • reinforcement learning for recommender systems
  • affective computing in recommender systems

Related Special Issues

Published Papers (7 papers)

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Research

16 pages, 14894 KiB  
Article
Large Language Models as Recommendation Systems in Museums
by Georgios Trichopoulos, Markos Konstantakis, Georgios Alexandridis and George Caridakis
Electronics 2023, 12(18), 3829; https://doi.org/10.3390/electronics12183829 - 10 Sep 2023
Cited by 4 | Viewed by 3558
Abstract
This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or [...] Read more.
This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment. Full article
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17 pages, 3972 KiB  
Article
Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network
by Jingtong Liu, Huawei Yi, Yixuan Gao and Rong Jing
Electronics 2023, 12(16), 3495; https://doi.org/10.3390/electronics12163495 - 18 Aug 2023
Viewed by 975
Abstract
Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper [...] Read more.
Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model. Full article
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18 pages, 2154 KiB  
Article
QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process
by Guosheng Kang, Bowen Liang, Junhua Xu, Jianxun Liu, Yiping Wen and Yun Kang
Electronics 2023, 12(12), 2575; https://doi.org/10.3390/electronics12122575 - 07 Jun 2023
Viewed by 668
Abstract
With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable [...] Read more.
With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results. Full article
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23 pages, 6073 KiB  
Article
HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation
by Mahesh Thyluru Ramakrishna, Vinoth Kumar Venkatesan, Rajat Bhardwaj, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Saima Anwar Lashari and Aliaa M. Alabdali
Electronics 2023, 12(6), 1365; https://doi.org/10.3390/electronics12061365 - 13 Mar 2023
Cited by 26 | Viewed by 1850
Abstract
Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and [...] Read more.
Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues. Full article
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30 pages, 8944 KiB  
Article
Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire
by Joaquim Neto, António Jorge Morais, Ramiro Gonçalves and António Leça Coelho
Electronics 2022, 11(21), 3466; https://doi.org/10.3390/electronics11213466 - 26 Oct 2022
Cited by 4 | Viewed by 1542
Abstract
The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has [...] Read more.
The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%. Full article
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28 pages, 1327 KiB  
Article
A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems
by Nikzad Chizari, Niloufar Shoeibi and María N. Moreno-García
Electronics 2022, 11(20), 3301; https://doi.org/10.3390/electronics11203301 - 13 Oct 2022
Cited by 11 | Viewed by 2580
Abstract
Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to [...] Read more.
Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company’s reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model’s performance. Full article
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16 pages, 2643 KiB  
Article
Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation
by Suzhi Zhang, Zijian Bai, Pu Li and Yuanyuan Chang
Electronics 2022, 11(18), 2966; https://doi.org/10.3390/electronics11182966 - 19 Sep 2022
Cited by 4 | Viewed by 1542
Abstract
With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation [...] Read more.
With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation system has been applied to many real-life scenarios, such as life services and autonomous driving. Specifically, the goal of POI recommendation is to recommend locations that match their personalized preferences to users. In existing POI recommendation methods, people tend to pay more attention to the impact of temporal and spatial factors of POI on users, which will alleviate the problems of data sparsity and cold start in POI recommendation. However, this tends to ignore the differences among individual users, and considering only temporal and spatial attributes does not support fine-grained POI recommendations. To solve this problem, we propose a new Fine-grained POI Recommendation With Multi-Graph Convolutional Network (FP-MGCN). This model focuses on the content representation of POIs, captures users’ personalized preferences using semantic information from user comments, and learns fine-grained representations of users and POIs through the relationships between content–content, content–POI, and POI–user. FP-MGCN employs multiple embedded propagation layers and adopts information propagation mechanisms to model the higher-order connections of different POI-related relations for enhanced representation. Fine-grained POI is finally recommended to users through the three types of propagation we designed: content–content information propagation, content–POI information propagation, and POI–user information propagation. We have conducted detailed experiments on two datasets, and the results show that FP-MGCN has advanced performance and can alleviate the data sparsity problem in POI recommendation tasks. Full article
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