A hybridized semantic trust-based framework for personalized web page recommendation

Deepak, Gerard; Shwetha, B. N.; N, Pushpa C; J, Thriveni; Venugopal, K R

doi:10.1080/1206212x.2018.1480472

Cited by 22 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A knowledge representation that is capable of successfully conveying the real meaning of a web page is called ontology. This study proposes strategies for the partial-automatic generation of ontologies within the context of a customized system framework for web pages that is based on web mining technologies [11].…”

Section: Iintroductionmentioning

confidence: 99%

Web Page Recommendation System by Integrating Ontology and Stemming Algorithm

Ayoobkhan¹,

Ali²

2022

IJASIS

View full text Add to dashboard Cite

In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.

show abstract

Section: Iintroductionmentioning

confidence: 99%