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Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat-Thalmann
Proceeding of the 36th international ACM SIGIR conference
随着移动设备的日益普及和大量基于地理位置的社交应用被用户所使用，人们在互联网上留下了大量的移动数据，尤其是用户的签到信息。基于这些签到信息，互联网公司可以为消费者及商家提供优质的基于地理位置的个性化服务，例如给消费者提供个性化的推荐服务，帮助其找到新颖的有趣的娱乐场所，同时为商家提供潜在的消费人群，定制个性化的广告等。
这篇文章主要的创新点包括以下三点：
| 符号 | 说明 |
|---|---|
| U,L,T | user set, POI set, time slot set |
| u,v,l,t | user \(u,v\in U\), POI \(l \in L\), time slot \(t \in T\) |
| \(c_u\),\(c_{u,t}\) | the binary check-in vector of u over L, and the binaray check-in vector u over L at t |
| \(c_{u,l}\), \(c_{u,t,l}\) | element of \(c_u\) and \(c_{u,t}\), respectively |
| \(w_{u,v}\) | the similarity between u and v |
| \(w^{(t)}_{u,v}\), \(w^{(ts)}_{u,v}\) | time-enchanced similarity, smoothed similarity |
| \(dis(l_i,l_j)\) | distance between \(l_i\) and \(l_j\) |
| wi(dis) | the willingness a user visits a dis far away POI |
| \(CI_l\), \(CI_{l,t}\) | the set of check-ins at l, \(CI_l\) at time t |
Time-aware Point-of-interest Recommendation
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原文地址：http://www.cnblogs.com/momonga/p/4776846.html
