基于轨迹相似度的伴随人员推荐

移动网络和智能终端的发展使得基于优质用户的伴随人员的推荐成为互联网发展的热点之一，而伴随人员的推荐算法则是至关重要的因素.针对以往基于地理位置的用户轨迹性相似推荐算法中需基于地理位置或基站数据，且数据稀疏时推荐结果不理想的问题，提出了基于IP场所的轨迹余弦相似度的伴随人员推荐，以更完善的IP场所数据代替地理位置数据，以一段时间的纵向日期和横向时刻分别计算余弦相似度以消除数据稀疏性问题.最后推荐出了相似度质量更高的伴随人员.

Companion Recommendation Based on Trajectory Similarity

With the development of mobile networks and intelligent terminals, the recommendation of the companion based on high-quality users has become one of the hot topics in the Internet, and the recommendation algorithm about companion is the crucial factor. In the past, the user location trajectory similarity recommendation algorithm was mainly based on geographic location or base station data and the data sparse may result in undesirable results. This paper proposes a companion recommendation model based on the cosine similarity of IP sites. More comprehensive IP sites data have been used instead of geographic data, and the date time data are calculated for cosine similarity to eliminate the data sparseness problem. Finally, the people with higher similarity and higher quality are recommended.