Abstract
Web page recommendation is one of the vital strategies in the era of Web 3.0 due to the exponential increase of the contents over the World Wide Web (WWW). In this paper CIPWR framework for recommendation of web pages which incorporates upper ontology generation, ontology alignment using the Lin similarity with an appropriate threshold and SynSet generation has been put forth. The framework integrates two topic models or encopresis the concept of Bi-topic modelling with two distinct topic models namely the Latent Dirchlet Allocation (LDA) and Latent Semantic Indexing (LSI) for lateral enrichment of topics and also the NELL and YAGO knowledge stores are integrated for Query word enrichment which is further used for feature selection and a Random forest classifier which is a strong machine learning driven feature controlled classifier has been added for the classification of the dataset and semantic similarity is computed by amalgamating cosine similarity and Jaccard similarity with the differential thresholds in order to yield the best in class results of 97.81% of average accuracy, 96.18% of average precision, 99.43% of average recall, 97.78% of average F-measure and the lowest FDR of 0.04 has been accomplished by the proposed CIPWR framework.
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Manoj Kumar, H.S., Deepak, G., Santhanavijayan, A. (2023). CIWPR: A Strategic Framework for Collective Intelligence Encompassment for Web Page Recommendation. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_22
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