STAN: Spatial-Temporal-Attention-Network-for-Next-Location-Recommendation
Update! The paper is accepted by the Web conference 2021. Paper: https://arxiv.org/abs/2102.04095. Oral: https://www.youtube.com/watch?v=ajNzESvOvzs.
Author Reply: Thank you for your interest in our work! Before asking questions regarding the codes or the paper, I strongly recommend you to read the FAQ first.
The running speed of STAN is extremely low. (We have a huge memory of the location matrix and a long sequence to learn) Try a proportion of users to test the performance. Increase the embed_dim accordingly if using more users.
Run "load.py" first and then "train.py". You should see something on the screen like this:
100%|██████████| 100/100 [14:32<00:00, 8.72s/it]
epoch:27, time:23587.941201210022, valid_acc:[0.18 0.49 0.56 0.67]
epoch:27, time:23587.941201210022, test_acc:[0.15 0.46 0.59 0.67]
I summarize some FAQs:
Q1: Can you provide a dataset?
A1: Our datasets are collected from the following links. Please feel free to do your own data processing on your model while comparing STAN as baseline.
http://snap.stanford.edu/data/loc-gowalla.html;
https://www.ntu.edu.sg/home/gaocong/data/poidata.zip; (Some people mention that this link is invalid, for reason I do not know neither. )
http://www-public.imtbs-tsp.eu/~zhang_da/pub/dataset_tsmc2014.zip
Q2: I ran into some problems in reading the paper or implementing the codes. May I talk/discuss with you?
A2: It would be my pleasure to answer your questions. Please do not hesitate to email me or leave comments at any time and explain the problem concisely so I can assist. Also, we hope the oral speech may resolve your questions.
Q2.1: What does it mean "The number of the training set is 𝑚 − 3, with the first 𝑚′ ∈ [1,𝑚 − 3] check-ins as input sequence and the [2,𝑚 − 2]-nd visited location as the label"?
A2.1: We use [1] as input to predict [2], use [1,2] as input to predict [3], and ..., until we use [1,...,m-3] to predict [m-2]. Basically we do not use the last few steps and reserve them as a simulation of "future visits" to test the model since these last steps are not fed into the model during training.
Q2.2: Can you please explain your trajectory encoding process? Do you create the location embeddings using skip-gram-like approaches?
A2.2: Pre-training of embedding is an effective approach and can further improve the performance for sure. Unfortunately, the focus and contribution of this paper are not on embedding pre-training but on spatio-temporal linear embedding, and pretraining is not used in baselines, so we do not use it in our paper. Nevertheless, it will be a contribution if you conceive new ideas to improve embedding efficiency.
Q2.3: Would it be better to construct edges based on spatial distances instead of using distances?
A2.3: If the edges can truly reflect the relations between each loaction and each user, then yes. Ideal 0-1 edge relation is a stronger representation. However, constructing edges merely based on spatial distances can raise problems. Consider that a 30-kilometer metro takes less time than a 5-kilometer walk. From the data, we only know distances.
Q2.4: What do you mean by setting a unit spatiotemporal embedding?
A2.4: 
Q2.5: What does each column/row in NYC.npy mean?
A2.5: Each row: [user id, check-in location id, time in hours].
Q2.6: Can we try a different division of train/dev/test datasets?
A2.6: Our goal here is to generalize for the future visits of each user we have known (we do not want to test the model performance on biased past behavior), instead of generalizing to other users whose user-id embeddings are not known to the model. Unlike the golden rules like "better location leads to higher price" that apply to any real estates in the world, each user has personalized behavioral pattern.
Q2.7: How is the value of the recall rate calculated in your paper? For example, the top5 probability of the NYC data set is 0.xx but in the paper it is 0.xxxx.
A2.7: It is common practice to run under different seeds and get the average value. We averaged the ten times results and all of them are accepted by the statistical test of p=0.01.
Q3: What is the environment to run the code? And version?
A3: We use python 3.7.2, CUDA 10.1 and PyTorch 1.7.1. Make sure to install all libs that we import.