Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: Summary:
The paper focus on building a Point-of-interest (POI) predictor based on temporal, semantic, social and geographical contexts fitted into a deep learning model with aim of overcoming sparsity issue in mobility data. Datasets used are publicly available and results, ablation studies supports the method's efficacy. This is an application paper and has shared code in zip.

Claimed contributions:
1. Auxiliary trajectory forecasting is incorporated in a POI prediction model as a first.
2. A novel consistency loss function to connect the predicted POI (from the main task) and the predicted location (from the auxiliary task).

main_review: 
In general, the work is encouraged, however the paper in its current shape needs some overhaul specifically towards scientific contribution.

Strengths:
1. Code, though small sized is available online.
2. Section 4.4 
3. Experiments and Supplementary material.

Relevance:
Relevant to NeurIPS application track.

Novelty:
Loss L3 for training MobTCast is sound.

Impact:
On prediction of crowds (useful in pandemic) and also tour planners possibly.

Weaknesses:
1. Writing the paper and linking and describing sections need improvement.
2. Lat, Lng and distances are based on certain geometric models of earth as earth's shape is Geoid. Google Maps uses one, Satellite, Navy, Flights others. What system is adhered here is not clear and have not been thought of it seems. What happens if POI spans across large regions and small regions?
3. Why can't this problem be modeled as a recommender problem? Justify.
4. How are timestamps validated across time-zones? Are they all in GMT or offset taken care of?
5. Analysis is missing from Experimental section.

Correctness:
1. Line 51 - "For example, people are more likely to visit Food POIs at lunch time than at other times. " - should not this be temporal context?
2. Line 58 - "manually designed functions" - usually human designed functions wrt semantics are better than those learnt by automation? Defend.
3. Line 184 - something works here do not mean it will work somewhere. An easy example to relate - deep learning works well for images, fails miserably for signals.
4. Line 216 - different recommender problems have their own inherent characteristic. Like music has genre and shopping has market basket influence.
5. Line 246 - extra softmax layer not clear.
6. Line 293: why Adam optimizer?

Effort: Effort has been made more in writing the paper more than that put in coding and thought process (philosophy). Reverse was expected.

Suggestions:
0. Give an example to illustrate the problem.
1. This is a technical paper venue. Your abstract should contain numbers instead of promising results. Directly jump to the point and results and the philosophy. Elaboration can be done in Introduction.
2. The long sentences should be broken into small sentences for clarity, continuity and smooth reading. May be another round of proof reading will enhance the grammatical and contextual use of words.
3. It is recommended to give a diagram in 'Introduction' section to make sense of the problem statement and application at a glimpse.
4. May be related work gaps can be enlisted in a table and what extra / new has been done be highlighted, focusing on whether new work is at all needed or just a feature nice to have?
5. Overcome repetitions - there exists many text not needed again - focus more on the technical content. 
6. Make contribution section crisp. You have introduced novel method - so what? Why others will use yours instead of their own? Highlight this in your preferred way.
7. De-clutter useless information - Line 130 - For example, in computer vision, it has 131 been used for semantic segmentation [25, 5], crowd counting [45], depth completion [27], and visual localisation [34]. - wastage of references and text area.
8. Follow Train-Eval-Split following Andrew Ng's ML Yearning book.
9. How does demographics and world events bias the POI prediction? A scope of future work.

limitations_and_societal_impact: There is no negative impact per se, apart from privacy issue of mobility data that may not be fully anonymized following k-anonymity logic. In terms of current pandemic scenario, this work may find some value in application across contact tracing and crowd management - hence has a social impact side.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposed a novel deep learning-based architecture for the next POI prediction. The proposed approach models the temporal and semantic contexts through Transformer, extracts the social context through self-attention module, and model the geographical context via an auxiliary task. Experiments show that it can achieve better results than the SOTA next POI prediction.
main_review: Originality:
This paper has novel design in the POI prediction model architecture. The key different is that the geographical context is captured through an auxiliary task instead of the manually designed kernels.

Quality:
The technique is somewhat sound. The experiments are quite comprehensive, it compares the proposed approach with other 6 approaches on 3 different datasets.

Clarity:
The paper is well-written, most of the points are clearly addressed. Refer to the limitation comments for some points that are not well addressed.

Significance:
This paper seems to address a difficult task in a better way than the previous work. Future works can compare this work as a new POI prediction baseline.
limitations_and_societal_impact: Limitations

1. Some important details of model training are missing. For example, given the social context extractor is using the mobility feature extractor, how do we train them together? Is it possible that if we don't train them together from scratch, we could get get better results? Say, in the first several iterations, we train the mobility feature extractor first and then start from the well-trained mobility feature extractor to train the full model. Basically there are connections among different modules in the model, what's the best training recipe?

2. What's the computation cost of the proposed approach, and compared with other approaches?
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: In the submitted manuscript, the authors proposed a Transformer-based method to predict the next POI (place-of-interests) of a sequence. The model takes into account not only the temporal sequence of previously visited POIs but also semantic, geographical, and extracted social information. Moreover, an auxiliary trajectory prediction task is introduced to increase the performance of the main task.
To prove the efficacy of the proposed strategy, the model is tested against three publicly available datasets: Gowalla, FS-NYC, and FS-TKY. Thanks to the modeling of the additional context information, MobTCast is able to outperform state-of-the-art results by a 7.22% margin with respect to previously proposed architectures.
main_review: Novelty:

The main originality of the paper is the usage of the auxiliary task of location prediction in addition to the POI prediction task. Otherwise, either the social/semantic context embedding or the application of transformer encoding are previously used in related works.

Quality: 

The paper provides a sufficient description of the proposed technique and the used functions. It also provides an experimental evaluation compared to previous work and ablation experiments to show the importance of the integrated parts in the model. However, the work has some weakness-points:
- Nothing is delivering the performance of the auxiliary task; for example, how accurate is the predicted location or how close is it to the site of the predicted POI.
- It is not clear how the results of the previous works in the comparison are obtained.
- The paper reports a single number (7.22%) of improvement over the three used datasets without specifying the number of top predictions used (top-k). It would be more informative if the gain is computed for each value of k and each dataset separately.
- The model explicitly include four context "types": temporal, semantic, social, and geographical. Following a data-driven approach, social links and neighbors are extracted from visited POIs. However, the same reasoning does not hold for the semantic and geographical context. What if they are not provided? It would be interesting to evaluate the model's robustness when making predictions using inferred semantic classes instead of ground-truth ones.

Clarity:

In general the paper is clear but it contains few writing typos and could benefit of some clarifications:
- Line 270: average -> averaged
- Figure 1 is a bit confusing. It could be improved using a lighter notation and a more symmetrical layout. The legend is also difficult to interpret, and coloured shapes should be put closer to their definitions.

Significance:

From the reported results, the proposed model has noticeable an improvement over the compared previous work, but some clarification is required for how they obtained the previous work results.
limitations_and_societal_impact: A related negative social impact to the POI prediction is the privacy of the users. The work did not discuss how the users' privacy is assured and in what context should this prediction model be used.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper proposes a Transformer-based context-aware network for human mobility prediction. Specifically, the authors explored the influence of four types of context in mobility prediction: temporal, semantic, social, and geographical contexts. A base mobility feature extractor is designed using the Transformer architecture, which takes both the history POI sequence and the semantic information as input. A self-attention module to model the influence of the social context. Moreover, the authors proposed to use an auxiliary task to model the geographical context and predict the next location. In the experimental results, the proposed method outperforms other state-of-the-art next POI prediction methods and illustrates the value of including different types of context in the next POI prediction.
main_review: The paper is generally well written and easy to follow. The detailed comments are listed below.

-- Originality: There seems two major novelty and contribution in this paper. One is to employ an auxiliary trajectory forecasting task to predict the geographical coordinates of the next most likely location. The other is to propose a novel consistency loss function to connect the predicted POI and the predicted location, which further improves the POI prediction performance. I think that these two ideas are interesting and novel. The authors provided an informative literature review on related work and discussed major differences between the proposed method and existing approaches.

-- Quality and Significance: The authors conducted extensive experiments on three widely used datasets and demonstrated promising results, which can support the claims of the authors. The comparison with state-of-the-art baselines and ablation baselines is illustrated with sufficient details. 

-- Clarity: The paper is clearly organized and well written.
limitations_and_societal_impact: The authors discussed the limitations of the proposed method and provided some insights on the future directions for improving the current method. However, there seems no discussion on the potential societal impact.
review_title: null

Meta-review: This paper proposes a transformer-based predictor for future places of interest. Its initial scores were 7, 6, 6, and 5; and they did not change during the discussion. The paper presents good empirical results. However, the reviewers were concerned that it is not very technically deep. I read the paper, and see both positives and negatives. Let me start with the positives:

1. Experiments in Table 1 are impressive. A lot of variability in baselines. Three datasets and top-$K$ for various $K$.

2. The ablation study in Table 2 shows that MobTCast performs well even without social context, and losses $L_2$ and $L_3$. In short, the loss $L_1$ is enough to be comparable or better than the state of the art. This indicates that the improvement is due to better sequence modeling using the transformer.

Now the negatives:

1. I am not sure what is the lesson learned from this paper. How would somebody who uses transformers benefit from this result? The paper is indeed not very deep.

2. I am not 100% sure that MobTCast improves over the state of the art because of the transformer. In particular, do any of the baselines use the semantic context in Section 4.1? If not, the observed improvement may be simply because of better features.

In my opinion, this paper should be judged as an application paper, and it clears the bar because of good empirical results. I strongly suggest that the authors take my comments into account, in addition to the detailed comments of the reviewers. Congratulations!
Decision: Accept (Poster)