SemMovieRec

Abstract

Recommendation System has been developed with the growth of Word Wide Web. Recently, Web3.0 or Semantic Web has changed the traditional way of its related approaches, by leveraging knowledge of Linked Open data Cloud which consist of domain specific and cross domain interconnected datasets. It fabricates thousands of RDF triples and millions of links (external/internal) to connect this open source data. As per our literature survey we have found that the Recommender System based on Linked Open data Cloud does not deal with this Knowledge Base in an efficient manner because of the problem of data sparsity and inconsistency. which results due to automatic generation of Resource Description Format data from unstructured documents that leads to garbage data have no sense in recommending. This paper aims to explore a hybrid recommender which can be used as a rating predictor as well as movie recommender of RDF datasets. Also. we present a new model for Recommender System that not only utilizes DBpedia Knowledge Base but also remove the former problems in Recommender System by using a preprocessing technique for sparsity removal. To prove the correctness and accuracy of our model we have implemented and tested it over other previous methodologies. In order to make our algorithm efficient. we also used different data structure for storing and processing.

Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

“…These algorithms predict class labels from attributes. For example, kNN (Ahn & Amatriain, 2010;Ristoski, Loza Mencía, Paulheim, & Menc, 2014), decision trees (Khrouf & Troncy, 2013;Ristoski et al, 2014), logistic regression (Moreno et al, 2014;Narducci et al, 2013;Zhang, Wu, Sorathia, & Prasanna, 2014), Support Vector Machines (SVM) (Di Noia, Mirizzi, Ostuni, & Romito, 2012;Kushwaha & Vyas, 2014;V. V. Ostuni, Di Noia, Mirizzi, Di Sciascio, & Noia, 2014), random forest (Narducci et al, 2013;, naive Bayes (Schmachtenberg, Strufe, & Paulheim, 2014) and bayesian classifiers .…”

“…Memory-based algorithms recommends items based on the entire collection of previously rated path queries. For example, rating prediction (Kushwaha & Vyas, 2014;Moreno et al, 2014;Narducci et al, 2013); singular value decomposition (SVD) (Ko & Son, 2015;Moreno et al, 2014;Rowe, 2014); implicit feedback; and matrix factorization (Lommatzsch, Kille, Albayrak, & Berlin, 2013).…”

“…Machine learning algorithms are time-consuming for the training phase, and some of them only use linked data just for representation, so they do not take into account the intrinsic semantic structure of datasets (Arturo et al, 2014;Ristoski et al, 2014). Other algorithms require user's profile information to produce recommendations, they suffer from the cold-start problem (Kushwaha & Vyas, 2014;Moreno et al, 2014). Finally, existing hybrid recommendation techniques are not organized in a conceptual architecture based on their functionalities.…”

Allied

“…• Supervised: a model is prepared through a training process where it produces predictions about class labels from attributes. kNN (Ristoski, Mencía, & Paulheim, 2014), decision trees (Ostuni, Di Noia, Di Sciascio, & Mirizzi, 2013;Ristoski et al, 2014); logistic regression (Ostuni et al, 2013;Moreno et al, 2014;Musto et al, 2014); SVM (Kushwaha & Vyas, 2014;Ostuni, Di Noia, Mirizzi, Di Sciascio, & Noia, 2014;Khrouf & Troncy, 2013); random forest (Ostuni et al, 2013;Musto et al, 2014); and naive Bayes (Schmachtenberg, Strufe, & Paulheim, 2014). • Unsupervised: input data is not labelled and does not have a known result, so they aim to discover the structure or distribution of the data.…”

“…Algorithms for rating predictions based on the entire collection of previously rated path queries. Rating prediction (Kushwaha & Vyas, 2014;Moreno et al, 2014;Musto et al, 2014); SVD (Moreno et al, 2014;Ko, H. G, Son, J., & Ko, I.Y. 2015); and matrix factorization (Lommatzsch, Kille, & Albayrak, 2013).…”