Research on LBS-based Optimizing Personal Recommendation System
Enhui Li, Xin Chen, Taiyan Hao, Xiangling Fu
Available Online July 2013.
- https://doi.org/10.2991/cse.2013.56How to use a DOI?
- LBS; collaborative filtering; personal recommendation; optimizing personal recommendation
- In recent years, more and more internet companies trend to fuse their products with personal recommendation functions which based on a popular technology called LBS (location based service).Although the idea of this business model satisfies the characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect of the personal recommendation is worse than they expected. Due to the current technology which is not suitable and mature to make a real combination between personal recommendation and LBS module to support the internet applications, there are plenty of problems such as the network environment of the user, the location filtering, user modeling, data sparse and cold start. In this paper, we proposed a novel optimized personal recommendation system, which based on LBS. The system can not only solve the major problems mentioned above but also provide a good recommendation services based on customer’s geographical position. According some statics we evaluate our system in an analog experiment and come to an conclusion that the new one provide a better performance than the traditional ideas.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Enhui Li AU - Xin Chen AU - Taiyan Hao AU - Xiangling Fu PY - 2013/07 DA - 2013/07 TI - Research on LBS-based Optimizing Personal Recommendation System BT - 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013) PB - Atlantis Press SP - 251 EP - 255 SN - 1951-6851 UR - https://doi.org/10.2991/cse.2013.56 DO - https://doi.org/10.2991/cse.2013.56 ID - Li2013/07 ER -