Applications of user and context-aware recommendations using ontologies

Naudet, Yannick; Schwartz, Lou; Mignon, Sabrina; Foulonneau, Muriel

doi:10.1145/1941007.1941038

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…Collaborative filtering methods are based totally on gathering and analysing a large amount of data on user's behaviours, activities, or preferences and predicting what users will like based totally on their similarity. Some well-known e-commerce web sites propose such recommendations with success factors, based on a record of user actions, user ratings, or correlations between different users (collaborative filtering principles) [12]. Some recommender frameworks, for example, Movielens [13], depend on collaborative filtering to customize the proposal of things.…”

Section: Collaborative Based Filteringmentioning

confidence: 99%

“…To ensure interoperability with third-party services and applications. For instance, Yannick et al, [12] used an ontology-based approach to express not only the interests of the user for anything described in an ontology but also the target of content for anything described in an ontology. The target mechanism was used in this paper was to support context-based recommendations and can be used to support other matching strategies.…”

Section: 3mentioning

confidence: 99%

“…Numerous candidate items are compared with items previously rated by the user, and the best match result items are recommended. Yannick et al, [12] used a content-based algorithm to filter the best result recommended to a user.…”

Section: Content-based Recommender Systemsmentioning

confidence: 99%

“…Similarly, [12] presented a personalization system in his work, which used ontology to represent knowledge and matchmaking algorithm for multimedia content recommendations. The target mechanism was used to support the context-based recommendation.…”

Section: Analysis Of Publicationsmentioning

confidence: 99%

“…They combined the ontology-based personalization system with context-aware recommendations to give accurate suggestions. Yannick et al, [12] proposed an ontology-based matchmaking approach for context-aware recommendations. They have presented a survey of an ontology set and a matchmaking algorithm that makes accurate recommendations by keeping a given user profile and situation, interests, and associated context.…”

Section: Analysis Of Publicationsmentioning

confidence: 99%

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A Review of Content-Based and Context-Based Recommendation Systems

Javed¹,

Shaukat

Hameed

et al. 2021

Int. J. Emerg. Technol. Learn.

188

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Section: Collaborative Based Filteringmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

Section: Content-based Recommender Systemsmentioning

confidence: 99%

Section: Analysis Of Publicationsmentioning

confidence: 99%

Section: Analysis Of Publicationsmentioning

confidence: 99%

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