Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

Leiva, Mario A.; Budán, Maximiliano Celmo David; Simari, Gerardo I.

doi:10.1109/mis.2020.2999569

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…This model highlights the embedding and transformation of item features. The transfer process from the input layer to the next is represented as shown in Equation (2).…”

Section: Content-based Recommendationmentioning

confidence: 99%

“…In the context of the digital age, recommender systems (RSs) have arisen. RSs analyze data to discern user preferences for items and assist users in efficiently sifting through information [2], directing them towards the content that is most relevant and valuable for their interests. Currently, RSs are widely adopted and have provided many economic benefits.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey of Recommender Systems Based on Deep Learning

Zhou,

Xiong,

Chen

2023

Applied Sciences

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With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.

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“…This model highlights the embedding and transformation of item features. The transfer process from the input layer to the next is represented as shown in Equation (2).…”

Section: Content-based Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Recommender Systems Based on Deep Learning

Zhou,

Xiong,

Chen

2023

Applied Sciences

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“…The study of RS is an interesting and trendy area of study and has been researched in many fields. Some of these fields include human-computer interaction [9], machine learning [10,11], statistics [12], artificial networks [13], calculative trust [14][15][16][17][18], argumentation [19], among others. Recommender systems also have many application areas such as medicine.…”

Section: Related Workmentioning

confidence: 99%

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

Iwendi

Ibeke

Eggoni

et al. 2021

Int. J. Info. Tech. Dec. Mak.

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The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

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“…The third article entitled "Guidelines for the Analysis and Design of Argumentation-based Recommendation Systems" by Leiva et al 6 focuses on argumentation-based recommender systems. Argumentation-based recommender systems utilize argumentation-based tools to generate and analyze arguments for recommending a specific item based on a user's preference.…”

mentioning

confidence: 99%

The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

Wang

Pasi

et al. 2020

IEEE Intell. Syst.

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Sydney & IT IS OUR pleasure to share with you this special issue on intelligent recommendation with advanced artificial intelligence (AI) and learning, which includes eight articles published in the September/October issue of IEEE Intelligent Systems (IS). After our announcement in early August 2019 for this special issue, we received 40 submissions, only 8 ones out of which are accepted to be included in this special issue. After a long period of hard-work of review from anonymous reviewers and careful revisions from the authors, we finally achieve the current versions of the accepted papers published in this issue. First, we would like to sincerely thank all the contributors and the anonymous reviewers for their great efforts made on this special issue. Second, we express our sincere gratitude to the IS team led by Professor Venkatramanan Subrahmanian for their support and help to ensure the timely publication of this issue.

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Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

Cited by 7 publications

References 20 publications

A Comprehensive Survey of Recommender Systems Based on Deep Learning

A Comprehensive Survey of Recommender Systems Based on Deep Learning

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

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