A Collaborative Filtering Systems based on Personality Information

DOI

10.2991/iiicec-15.2015.163How to use a DOI?

Keywords

Recommender System; Collaborative Filtering; Personality; Cold Start

Abstract

As one of the most successful recommendation approaches, collaborative filtering (CF) has been attracting an increasing amount of interest in academia. However, the cold-start problem, where historical data is too sparse since new users have not rated enough items, limits the success of collaborative filtering in certain application domains. In this paper, an innovative item-personality-based collaborative filtering system based on personality information is proposed to resolve the cold-start problem. By incorporating human personality into the collaborative filtering framework, we improve the conventional user-based collaborative filtering method. By a linear combination of both user-based CF and item-personality-based CF, the predicted rating can be obtained. An experiment is conducted to evaluate the effectiveness. The results show that the proposed item-personality-based CF can considerably alleviate cold-start problem.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianwang Wang
PY  - 2015/03
DA  - 2015/03
TI  - A Collaborative Filtering Systems based on Personality Information
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 724
EP  - 727
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.163
DO  - 10.2991/iiicec-15.2015.163
ID  - Wang2015/03
ER  -