论文阅读——Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

00 文章基本信息 01 摘要

当前RNNs在处理neighbor check-ins时存在的弊端：

rarely consider the spatio-temporal intervals between neighbor
check-ins, which are essential for modeling user check-in behaviors
in next POI recommendation.换言之：现有的基于RNN的方法在对用户短期偏好建模时，要么忽略了用户的长期偏好，要么忽略了最近访问的poi之间的地理关系，使得我们的推荐结果并不可靠。

our contributions：

we propose a new Spatio-Temporal Gated Network (STGN) by
enhancing long-short term memory network;针对上述局限性，我们提出了一种新的长短期偏好建模方法（LSTPM）。two pairs of time gate and distance gate are designed to control the
short-term interest and the long-term interest updates, respectively;we introduce coupled input and forget gates to reduce the number of
parameters and further improve efficiency;we evaluate the proposed model using four real-world datasets from
various location-based social networks.

该论文的核心贡献：
为什么要加入时间门和距离门，原因就在于最近一次访问的POI并不一定就具有很强的说服力，比如，最近一次的访问距离现在时间很长（用户已经改变了以前的行为习惯）或者最近一次访问的距离太长了（比如我家海南，去黑龙江玩了一次，现在再给我推荐黑龙江的POI就不合适了）。
改进之处就在于考虑最近一次访问的POI的时间和距离间隔，如下图：

1 Introduction

以往的工作：
以往的工作大都是用对待sequential data analysis and recommendation的方式来处理next POI recommandation问题，但是这种处理方法存在一个很大的弊端：

它们没有考虑time intervals and geographical distances between neighbor
items，这也是next POI recommandation与other sequential tasks的不同之处所在。

因此，本文提出了a new Spatio-Temporal Gated Network by enhancing long short term memory, named STGN, to model users’ sequential visiting behaviors，该方法的优点如下：

Time intervals and distance intervals of neighbor check-ins are
modeled by time gate and distance gate;use the coupled input and forget gates to reduce the number of
parameters, making our model more efficient

最后，总结本文的contributions：

We propose an innovative gate mechanism way to model
spatio-temporal intervals between check-ins under LSTM
architecture to learn user’s visiting behavior for the next POI
recommendation.A STGN model is proposed to incorporate carefully designed time gates
and distance gates to capture the spatio-temporal interval
information between check-ins. As a result, STGN well models user’s
short-term and long-term interests simultaneously;Experiments on four large-scale real-world datasets are conducted to
evaluate the performance of our proposed model. Our experimental
results show that our method outperforms state-of-the-art methods. 2 Related Work 2.1 POI Recommendation

1.POI Recommendation

Different from traditional recommendations (e.g., movie
recommendation, music recommendation)POI recommendation is characterized by geographic information and
no explicit rating information（不明确的评级信息）;additional information, such as social influence,temporal information, review information, and transition between POIs, has been leveraged for POI recommendation.

2.Next POI recommendation

a natural extension of general POI recommendation;

sequential influence between successive check-ins plays a crucial
role in next POI recommendation;

2.2 Neural Networks for Recommendation

卷积神经网络CNN已经用于POI推荐系统。

3 Preliminaries

下面给出Next POI recommandation的形式化定义以及LSTM的简介。

3.1 Problem Formulation

Next POI recommandation的关键在于推荐的下一个POI跟时间有关，其实跟常规的POI recommandation相比，是换汤不换药的。
问题定义如下：

用户集：
POI集：
用户u的历史POI访问序列：

其中，
表示用户u在时间ti时访问POI v。

Next POI recommandation问题的目标就是：
根据用户的历史访问记录，为用户推荐一个其在时间ti可能会访问的unvisited名单。

3.2 LSTM

过于复杂，不展开讲解，可简单参考https://blog.csdn.net/qq_33429968/article/details/114416796?spm=1001.2014.3001.5501。

4 Our Approach 4.1 Spatio-Temporal Gated Network

其核心改变就是在LSTM的基础上加了两对time和distance gates，用来控制最近一次访问的POI对下次推荐的影响。第一对time/distance gate控制short-term interest，第二对time/distance gate控制long-term interest。

为什么要加入时间门和距离门：
原因就在于最近一次访问的POI并不一定就具有很强的说服力，比如，最近一次的访问距离现在时间很长（用户已经改变了以前的行为习惯）或者最近一次访问的距离太长了（比如我家海南，去黑龙江玩了一次，现在再给我推荐黑龙江的POI就不合适了）。
改进之处就在于考虑最近一次访问的POI的时间和距离间隔，如下图：

4.2 Variation of Coupled Input and Forget Gates 4.3 Training 5 Experiments 6 Conclusions