A Study on Ontology Based Collaborative Filtering Recommendation Algorithms in E-Commerce Applications

Mohana, H. S.; Suriakala, M.

doi:10.9790/0661-1904011419

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…La recommandation éditoriale désigne les situations dans lesquelles la proposition spécifique de certains contenus est fondée sur la mention manifeste d'un choix assumé de l'entreprise dans le dispositif. Avec le numérique, la possibilité d'exploiter une grande quantité de données d'usage sur les comportements des internautes a conduit à l'émergence d'une nouvelle forme de recommandation personnalisée associée aux potentialités des traitements algorithmiques automatisés (Mohana et Suriakala, 2017). Au fur et à mesure de l'évolution des plateformes de diffusion numérique, ces outils de recommandation personnalisée se sont développés, et ce sont eux qui suscitent le plus d'inquiétudes.…”

Section: Notes De Rechercheunclassified

De l’information aux industries culturelles, l’hypothèse chahutée de la bulle de filtre

Farchy,

Tallec

2023

questionsdecommunication

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La recommandation algorithmique fait débat depuis les premiers travaux qui ont popularisé le terme de « bulle de filtre ». L'hypothèse d'un enfermement de l'usager est largement associée aux biais liés à la personnalisation de plus en plus fine et de plus en plus poussée des résultats proposés, qu'il s'agisse des moteurs de recherche, des réseaux sociaux ou des services culturels. En matière d'information, la question soulevée est celle de l'accès au pluralisme des opinions dans une société démocratique. Pour l'offre proposée par les services culturels en ligne, la thèse des biais algorithmiques et de l'enfermement dans la même catégorie de contenus au détriment de la diversité culturelle, bien que récurrente, n'a jamais été totalement éprouvée dans des travaux empiriques. Établir des formes de régulation adaptées suppose pourtant des outils d'objectivation afin de confirmer ou d'atténuer les craintes exprimées par l'hypothèse de la bulle de filtre.Mots clés. -recommandation, streaming, bulle de filtre, diversité culturelle.

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Section: Notes De Rechercheunclassified

De l’information aux industries culturelles, l’hypothèse chahutée de la bulle de filtre

Farchy,

Tallec

2023

questionsdecommunication

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“…By the continued growth of e-commerce, recommender systems are being used to help users in prioritizing [10]. Collaborative filtering is one of the recommender systems to model and/or analyze consumers' prioritizing that suggests suitable recommendations [13]. Figure 3 presents the classification of recommender systems, and the highlights are shown in the mainstream of this research article [14].…”

Section: Related Research Work On Collaborative Filtering Recommendementioning

confidence: 99%

“…The most common criteria for evaluating recommender systems are Precision, Recall and F1 applied in Eqs. (11)(12)(13), respectively. Compared to the rating datasets, where users typically rate only a very small number of items, making the dataset extremely sparse, binary selected datasets are dense, as each item was either selected or not by the user α is the number of items that are considered related to the top of the recommended items for the user.…”

Section: The Dataset and Evaluation Matricesmentioning

confidence: 99%

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Item-Based Recommender Systems Applying Social-Economic Indicators

Atashkar

Safi-Esfahani

2020

SN COMPUT. SCI.

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Despite the development of online items in the market, Item-based Recommender Systems play an essential role to assist consumers to access their targets rapidly. Research activities show that users (consumers) in various societies have different purchasing behaviors. In the latest studies, the similarity of users was calculated based on different indications such as Cultural Indicators. Nonetheless, they neglect social-economic indicators expected to be more factors that are efficient. In this research, a framework was designed for recommender systems called SEIRS that include several algorithms based on social-economic indicators. In fact, the similarity of items was defined in terms of the similarity between users' evaluation based on social-economic indicators. The presented method categorizes items and recommends them to users so that they match with users' favorites more precisely. The results were compared not only with the standard item-based system but also with different mixtures of social-based and economical-based indicators. In addition, the presented idea was used to solve the cold start problem that occurs when a newcomer customer with no information on his previous purchases and evaluations becomes available. Furthermore, it is possible to present suggestions that are more precise by using the SVD algorithm to solve the data sparsity. The present research applied three datasets: first, internet purchase by individuals (isoc_ibuy) dataset, second the related data on social-economic indicators from OECD, and third the actual BookCrossing dataset. The experimental results represent that in comparison with the standard recommender system, by applying social indicators the proposed recommender algorithm improved precision by 17.19%, and economic indicators improved precision by 17.60%, respectively. Applying both social and economic indicators provides 20.91% more accurate recommendations.

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“…Furthermore, [4,5] categorizes the techniques based on suggestion factors. To increase the accuracy of CF recommendations, the researchers construct a semantic-level information model based on an ontological notion to address the issues mentioned above [6,7]. Furthermore, ontology recommender systems are classified according to the tool, ontology type, and ontology representative language in [3,4,8].…”

Section: Introductionmentioning

confidence: 99%

CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

Mhammedi

Massari

Gherabi

et al. 2023

Lecture Notes in Networks and Systems

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Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering suggestions. The main purpose of this work is to address the recommender system's data sparsity and accuracy problems by using the matrix factorization algorithm of collaborative filtering based on the dimensional reduction method and, more precisely, the Nonnegative Matrix Factorization (NMF) combined with ontology. We tested the method and compared the results to other classic methods. The findings showed that the implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.

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A Study on Ontology Based Collaborative Filtering Recommendation Algorithms in E-Commerce Applications

Cited by 9 publications

References 9 publications

De l’information aux industries culturelles, l’hypothèse chahutée de la bulle de filtre

De l’information aux industries culturelles, l’hypothèse chahutée de la bulle de filtre

Item-Based Recommender Systems Applying Social-Economic Indicators

CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

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