A Graph Model for Recommender Systems

DOI

10.2991/iccsee.2013.221How to use a DOI?

Keywords

recommender systems,graph model, collaborative filtering, resource allocation matrix

Abstract

With the constant enlargement of the scope and coverage of the Internet, the traditional search algorithms just help to filter data, without considering the needs of individuals. Therefore, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens, and finally verifies the results. The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does. Meanwhile, the algorithm can effectively mitigate the sparse of the rating matrix.

Copyright

© 2013, 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  - Hong Chen
AU  - Mingxin Gan
AU  - Mengzhao Song
PY  - 2013/03
DA  - 2013/03
TI  - A Graph Model for Recommender Systems
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 878
EP  - 881
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.221
DO  - 10.2991/iccsee.2013.221
ID  - Chen2013/03
ER  -