Paper Info
Title
SemMovieRec: extraction of semantic features of DBpedia for recommender system
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.
Year
DOI
Venue
2014
10.1145/2675744.2675759
COMPUTE
Keywords
Field
DocType
recalll,dbpedia,precsion,recommendation,linked open data,hybrid,information search and retrieval
Recommender system,Data mining,Data structure,Information retrieval,Source data,Computer science,Linked data,Semantic Web,Knowledge base,RDF,Cloud computing
Conference
Citations 
PageRank 
References 
5
0.45
19
Authors
2
Name
Order
Citations
PageRank
Nidhi Kushwaha173.21
Om Prakash Vyas2528.92