Offline Shop Recommendation based On Online Shopping History
Chongji Mo, Congcong Chen
Available Online March 2015.
- https://doi.org/10.2991/etmhs-15.2015.213How to use a DOI?
- Recommender system; O2O.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chongji Mo AU - Congcong Chen PY - 2015/03 DA - 2015/03 TI - Offline Shop Recommendation based On Online Shopping History BT - 2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) PB - Atlantis Press SP - 962 EP - 965 SN - 2352-5398 UR - https://doi.org/10.2991/etmhs-15.2015.213 DO - https://doi.org/10.2991/etmhs-15.2015.213 ID - Mo2015/03 ER -