Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a SpatioTemporal aware Embedding framework (STE) and applies to POIs (STEP) for successive POI recommendation. Specifically, the proposed STE framework consists of three parts: the context graph building strategies to construct simplified affinity graphs, the spatiotemporal model to extract the spatiotemporal features of check-ins, and the sequential model to extract sequential feature of check-ins. 
main_review: The introduced spatiotemporal model of STE, which consists of a grid-cell spatial encoder and a visiting time encoder, is capable of mining the POI-specific spatiotemporal characteristics. Finally, the authors implement successive POI recommendation systems based on the STEP using simple recurrent neural networks as recommenders. Experimental results on Instagram Check-in and Gowalla datasets demonstrate that STEP achieves the state-of-the-art successive POI recommendation performance. However, the novelty of this paper may be over-claimed. My detailed comments are as follows.

Positive points:
1.	The proposed conjunctive representing approach based on a unique spatiotemporal context graph solves the problem of previous spatiotemporal modeling methods in which spatial and temporal information are isolated and represented separately. 
2.	The proposed method does not need access to private information such as user preferences. 
3.	The experimental results on Instagram Check-in and Gowalla datasets demonstrate the proposed method outperforms baselines.

Negative points: 
1.	The motivation and novelty of the proposed method are not convincing.
1)	In the first contribution, the authors claim that they propose this method motivated by the graph-embedding strategy of structural knowledge in the entorhinal-hippocampal system, which is very confusing. The proposed sequential context graph $G_{seq}$ and spatial context graph $G_{spa}$ are very similar to temporal graph $G_t$ and spatial graph $G_s$ in STP-UDGAT[1], respectively. However, the authors do not explain anything about this.
2)	The authors design the grid-cell spatial encoder motivated by grid-cells in the entorhinal-hippocampal system. However, it is worth noting that the formulations of the grid-cell spatial encoder are the same as Space2Vec [2]. The authors only slightly claim that the number of grid scales following the previous work [2].
3)	According to [3], hippocampal place cells encode a geometric representation of space. It is very confusing to claim that hippocampal place cells represent perception conjunctions effectively in the abstract. As shown in Figure 1, why do the authors propose the spatiotemporal conjunctive representation motivated by place cells? More explanations on it may be better.

2.	The authors claim that POI $p_i$ and $p_j$ are spatiotemporal neighboring if they are spatial and temporal neighboring in Sec.3.2.2. However, why do average values of $E_{st}$ per POI exceed those of $E_{spa}$ per POI in Table 3?

Minor issues:

1.	In the Introduction, "Most importantly, we elaborate … an visiting time encoder …" should be "Most importantly, we elaborate … a visiting time encoder …".

2.	In the One-stage methods of Sec.4, “an LSTM variant” and “an LSTM-based method” should be “a LSTM variant” and “a LSTM-based method”, respectively.

References:

[1]	STP-UDGAT : Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. In Proceedings of the ACM International Conference on Information & Knowledge Management, pp. 845–854, 2020.  
[2]	Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells. In Proceedings of the International Conference on Learning Representations, 2020.  
[3]	The hippocampus as a predictive map. Nature Neuroscience, 20(11):1643–1653, 2018.  

summary_of_the_review: The introduced spatiotemporal model of STE, which consists of a grid-cell spatial encoder and a visiting time encoder, is capable of mining the POI-specific spatiotemporal characteristics. Finally, the authors implement successive POI recommendation systems based on the STEP using simple recurrent neural networks as recommenders. Experimental results on Instagram Check-in and Gowalla datasets demonstrate that STEP achieves the state-of-the-art successive POI recommendation performance. However, the novelty of this paper seems to be over-claimed.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposed a successive POI recommendation method inspired by entorhinal-hippocampal system.
main_review: Strengths
1. The writing is easy to follow.
2. The inspiration from the entorhinal-hippocampal system is interesting.

Weakness
1. In the experiment, how to construct user preference embedding for baselines is not clear.
2. What is the difference of the objective between the sequential model and time pattern model? It seems the sequential model can also capture temporal information.
summary_of_the_review: The inspiration is interesting. But some parts need further clarification. I suggest "weak reject".
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper proposed SpatioTemporal aware Embedding framework for POIs (STEP), which considers two types of POI-specific representations: sequential representation and spatiotemporal conjunctive representation. Specifically, the spatiotemporal conjunctive representation represents POIs from spatial and temporal aspects jointly.
main_review: Strengths:
The paper proposed a new model to utilize both spatial and temporal information of POIs to make Successive POI Recommendations.

Weaknesses:
1. The paper claims the proposed solution is based on the entorhinal-hippocampal system to process spatial and temporal information. However, spatial and temporal information has long been applied for POI embedding and recommendations. It seems the major difference between the proposed model has the theoretical support from entorhinal-hippocampal systems while others have not, but I cannot find such proof in the paper.

2. It's not clear to find a clear proof or discussion regarding the difference with other models in the use of spatial and temporal information.

3. The writing of Section 3 needs more clarification. It's unclear if the STEP model in Section 3 is learned per sequence, per user, or all records together. The description in Section 3.1 says "Given one POI and its context in the check-in sequence", my understanding is:
The sequential model (Sec. 3.1) G_seq is learned for each sequence, which refers to "one set of continuous checkins of one user in one day". 
The spatial model (Sec. 3.2) G_spa is learned for all POIs regarding their coordinates.
The temporal model (Sec. 3.3) is learned from all visits (from different sequences) to each POI.
The spatiotemporal neighboring considers both spatial model and temporal model. Thus, the recommendation highly prefers POIs in the top K closest neighbors which were also visited frequently by all users within a short time window.
summary_of_the_review: The paper needs to clarify the model definition and how each sub-model is integrated.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: To address the successive POI recommendation problem, this paper proposes a brain-inspired spatiotemporal-aware embedding method, called STEP, which learns the conjunctive representations over the unique spatiotemporal context graphs. The experimental results demonstrate the superiority against other SOTA methods
main_review: This paper proposes a spatiotemporal-aware embedding framework and apply to the successive POI recommendation problem. The proposed method, called STEP, is inspired by the mammalian brain entorhinal-hippocampal system that has the conjunctive representation mechanism. Thus, the main motivation of the STEP is to learn the conjunctive representations on the unique spatiotemporal context graphs, which is different from the previous methods dealing with the spatial and temporal information separately.

The experiments are conducted on two real-world datasets, and the experimental results show that the proposed STEP could significantly outperform other baselines including embedding-recommender methods and one-stage methods, although the STEP does not utilize the user preference information.

Overall, the proposed visiting time pattern encoding and spatiotemporal conjunctive embedding learning are interesting and useful, and the paper is well written. The concerns are list below:

1.	For the neighboring timestamps, it is unclear why the weekend time is not be considered.
2.	The paper claims that the proposed framework could be applied to other applications such as the wildlife preservation and urban traffic scheduling problems. However, it is better to give some more details about how to transfer the proposed method to other tasks. For example, the authors could provide a case study experiment on traffic scheduling problem.

summary_of_the_review: This paper is technically sound, and the experiments are sufficient. Thus, my rating for this paper is weak accept.
review_title: null

Meta-review: Despite some positive points, the criticisms (and overall scores) put this paper below the bar. The reviewers raise issues of novelty, as well as problems with the experiments and argue that some claims are unsupported.
Decision: Reject