loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Yuka Ikebe 1 ; Masaji Katagiri 2 and Haruo Takemura 3

Affiliations: 1 NTT DOCOMO and Inc., Japan ; 2 NTT DOCOMO, Inc. and Osaka University, Japan ; 3 Osaka University, Japan

ISBN: 978-989-8565-29-7

Keyword(s): Link Prediction, Matrix Factorization, Latent Feature, Semi-supervised Learning, One-class Setting.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining High-Dimensional Data ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: This paper describes a semi-supervised learning method that uses smartphone usage data to identify friendship in the one-class setting. The method is based on the assumption that friends share some interests and their smartphone usage reflects this. The authors combine a supervised link prediction method with matrix factorization which incorporates latent features acquired from the application usage and Internet access. The latent features are optimized jointly with the process of link prediction. Moreover, the method employs the sigmoidal function to estimate user affinities from the polarized latent user features. To validate the method, fifty university students volunteered to have their smartphone usage monitored for 6 months. The results of this empirical study show that the proposal offers higher friendship prediction accuracy than state-of-the-art link prediction methods.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.208.186.19

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ikebe, Y.; Katagiri, M. and Takemura, H. (2012). Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 199-205. DOI: 10.5220/0004133201990205

@conference{kdir12,
author={Yuka Ikebe. and Masaji Katagiri. and Haruo Takemura.},
title={Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={199-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004133201990205},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data
SN - 978-989-8565-29-7
AU - Ikebe, Y.
AU - Katagiri, M.
AU - Takemura, H.
PY - 2012
SP - 199
EP - 205
DO - 10.5220/0004133201990205

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.