An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

Benkaddour, Fatima Zohra; Taghezout, Noria; Kaddour-Ahmed, Fatima Zahra; Hammadi, Ilyes-Ahmed

doi:10.9781/ijimai.2018.06.003

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…According to [14], the choice of the similarity measure has a direct influence on the quality of the recommendations in terms of accuracy. The cosine similarity measure is the most widely used in clustering [15], [16], [3], [17] and information retrieval techniques [18], [19], [20]. Several comparisons have been established approving the quality of its performance [16], [21], [17], [22], [23].…”

Section: ) Recommendation Systemsmentioning

confidence: 99%

“…The cosine similarity measure is the most widely used in clustering [15], [16], [3], [17] and information retrieval techniques [18], [19], [20]. Several comparisons have been established approving the quality of its performance [16], [21], [17], [22], [23]. The evaluation of recommender systems has always been the focus of several researchers, resulting in different performance evaluation measures in addition to the criterion of the user's satisfaction.…”

Section: ) Recommendation Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Bourenane¹,

Houari²,

Taghezout³

2023

IJCDS

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The main objective of this study is to create a collaborative parallel environment, which supports the unsupervised classification and the filtration of an important volume of information. The proposed approach consists in the integration of an agent-based system composed of five reactive agents to assist the recommendation of the stored services in the cloud and to cluster these services through a new improved K-means as well. The conducted experiments and evaluations of the different approaches and measures, such as: Euclidean distance, Manhattan distance and Cosine similarity, show that the proposed approach of the unsupervised classification improve the within cluster sum of squares (WCSS), which facilitates the access to personalized and relevant services requested in a very improved response time, especially through migration to the cloud using agents.

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Section: ) Recommendation Systemsmentioning

confidence: 99%

Section: ) Recommendation Systemsmentioning

confidence: 99%

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Bourenane¹,

Houari²,

Taghezout³

2023

IJCDS

View full text Add to dashboard Cite

show abstract

“…In several fields, contextual information is adopted in recommendation services by an enormous number of organizations. Therefore, a more enhanced software development service based on the reusability of industrial datasets for recommendations and contextual suggestion systems is necessary [190]. For instance, in movie recommendations [56,64,67,177], the production, tourists with different tastes or genres, and the services to watch online are increasing at enormous speed.…”

Section: Recommendations For Industriesmentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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“…RS suggest to the users about the items they probably will like. Depending on the item nature, a variety of RS can be implemented: e-learning [2], tourism [3], [4], films [5], restaurants [6], networks [7], healthcare [8], industrial operators [9], etc. The most accurate type of RS is the Collaborative Filtering (CF) one [10], [11].…”

Section: A Recommendations To Individual Usersmentioning

confidence: 99%

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Bobadilla¹,

Gutiérrez²,

Alonso³

et al. 2020

IJIMAI

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In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.

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An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

Cited by 10 publications

References 17 publications

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

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