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Authors: Nidhi Kushwaha and O. P. Vyas

Affiliation: Indian Institite of Information Technology, India

ISBN: 978-989-758-106-9

Keyword(s): Matrix Factorization, Semantic Topics, DBpedia, RDF, SPARQL, Similarity Coefficient, TF-IDF.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Data Engineering ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Internet Technology ; Knowledge Management ; Ontologies and the Semantic Web ; Ontology and the Semantic Web ; Recommendation Systems ; Searching and Browsing ; Society, e-Business and e-Government ; Software Agents and Internet Computing ; System Integration ; User Modeling ; Web Information Systems and Technologies ; Web Interfaces and Applications

Abstract: The Matrix Factorization model proved as a state of art technique in the field of Recommender Systems. The latent factors in these techniques are mathematically derived factors that are useful in terms of dimensionality reduction and sparsity removal. In this paper, we exploited the information on these latent factors in addition with semantic knowledge fetched from the DBpedia dataset to predict the movies to users, based on their selected topics in the past. We incorporate matrix factorization with the Semantic information to increase the accuracy of the recommendation and also increase the contextual information into it. For handling cold start users, we also provide an opportunity for the user, to select topics at the run time and prediction will be made according to their selection. To improve the diversity of the prediction in both the cases we also used a specific strategy for the end user recommendation.

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Paper citation in several formats:
Kushwaha, N. and Vyas, O. (2015). SemTopMF - Prediction Recomendation by Semantic Topics Through Matrix Factorization Approach.In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 118-123. DOI: 10.5220/0005475001180123

@conference{webist15,
author={Nidhi Kushwaha. and O. P. Vyas.},
title={SemTopMF - Prediction Recomendation by Semantic Topics Through Matrix Factorization Approach},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2015},
pages={118-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005475001180123},
isbn={978-989-758-106-9},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - SemTopMF - Prediction Recomendation by Semantic Topics Through Matrix Factorization Approach
SN - 978-989-758-106-9
AU - Kushwaha, N.
AU - Vyas, O.
PY - 2015
SP - 118
EP - 123
DO - 10.5220/0005475001180123

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