An Ontology Based Recommender System to Mitigate the Cold Start Problem in Personalized Web Search

Makwana, Kamlesh; Patel, Jay; Shah, Parth

doi:10.1007/978-3-319-63673-3_15

Cited by 4 publications

(1 citation statement)

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“…WUM is further classified into web server log which is the data of access history stored by the server. To identify usage patterns of the user many pattern mining techniques can be employed which can discover relations between variables in huge databases [10]. In the proposed work, the technique PFCM Algorithm is used for mining frequent pattern of user logs based on the user behavior and according to the similarity of the content, the related web page is recommended to the user.…”

Section: Introductionmentioning

confidence: 99%

A Proficient Web Recommender System using Hybrid Possiblistic Fuzzy Clustering and Bayesian Model Approach

Venugopal¹,

Nagraj²

2018

IJIES

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In day to day life, the process of projecting the correct information to the authorized person is more difficult, which makes complexity to the decision making process. Web Page Recommendation Systems (WPRS) used in various fields to identify the customer needs and to help the users to take appropriate decisions over the service or product according to his/her preference. The group of users with similar preference will be identified by using Possibilistic Fuzzy C-Means (PFCM) algorithm with an S3I Similarity Measure (SM). The proposed method will determine the gain and loss of the web users based on the web directories which can be modified by using Relevance Feedback Bayesian Network (RFBN) technique. The experimental results are conducted on the MNSBC dataset and the outcomes are compared with the existing methods like Singular Value Decomposition (SVD) methods. The method predicts the accuracy up to 85% when compared with the existing methods and the outcome results proved the effectiveness of the PFCM -RFBN method.

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Section: Introductionmentioning

confidence: 99%