SemTopMF - Prediction Recomendation by Semantic Topics Through Matrix Factorization Approach

Nidhi Kushwaha, O. P. Vyas

2015

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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