Offline Shop Recommendation based On Online Shopping History

DOI

10.2991/etmhs-15.2015.213How to use a DOI?

Keywords

Recommender system; O2O.

Abstract

Most existing POI recommender systems is facing the problem of sparsely of data. In this paper, an O2O (Online to Offline) recommendation method is proposed, which can take advantage of users’ online shopping history data. User behavior is converted to user preference, modeled as user and interest-term matrix. Based on the model, the result list of offline shop is recommended by O2O recommendation algorithm. The algorithm is implemented, and experiments on the real dataset show the feasibility of the method.

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  - Chongji Mo
AU  - Congcong Chen
PY  - 2015/03
DA  - 2015/03
TI  - Offline Shop Recommendation based On Online Shopping History
BT  - Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science
PB  - Atlantis Press
SP  - 962
EP  - 965
SN  - 2352-5398
UR  - https://doi.org/10.2991/etmhs-15.2015.213
DO  - 10.2991/etmhs-15.2015.213
ID  - Mo2015/03
ER  -