融入时间的兴趣点协同推荐算法

兴趣点（POI）推荐可以帮助用户发现其没有访问过但可能感兴趣的地点，是重要的基于位置的服务之一。时间在POI推荐中是一个重要因素，而现有POI推荐模型并没有较好地考虑时间因素，因此通过考虑时间因素来提出融入时间的POI协同推荐（TUCF）算法，从而提高POI推荐的效果。首先，分析基于位置的社交网络（LBSN）的用户签到数据，以探索用户签到的时间关系；然后，利用时间关系对用户签到数据进行平滑处理，以融入时间因素并缓解数据稀疏性；最后，根据基于用户的协同过滤方法，在不同时间推荐不同POI给用户。在真实签到数据集上的实验结果表明，与基于用户的协同过滤（U）算法相比，TUCF算法的精确率和召回率分别提高了63%和69%；与具有平滑增强时间偏好的协同过滤（UTE）算法相比，TUCF算法的精确率和召回率分别提高了8%和12%；并且TUCF算法的平均绝对误差（MAE）比U算法和UTE算法分别减小了1.4%和0.5%。

Time-incorporated point-of-interest collaborative recommendation algorithm

Point-Of-Interest (POI) recommendation aims to recommend places that users do not visit but may be interested in, which is one of the important location-based services. In POI recommendation, time is an important factor, but it is not well considered in the existing POI recommendation models. Therefore, the Time-incorporated User-based Collaborative Filtering POI recommendation (TUCF) algorithm was proposed to improve the performance of POI recommendation by considering time factor. Firstly, the users' check-in data of Location-Based Social Network (LBSN) was analyzed to explore the time relationship of users' check-ins. Then, the time relationship was used to smooth the users' check-in data, so as to incorporate time factor and alleviate data sparsity. Finally, according to the user-based collaborative filtering method, different POIs were recommended to the users at different times. Experimental results on real check-in datasets showed that compared with the User-based collaborative filtering (U) algorithm, TUCF algorithm had the precision and recall increased by 63% and 69% respectively, compared with the U with Temporal preference with smoothing Enhancement (UTE) algorithm, TUCF algorithm had the precision and recall increased by 8% and 12% respectively. And TUCF algorithms reduced the Mean Absolute Error (MAE) by 1.4% and 0.5% respectively, compared with U and UTE algorithms.