Match prediction from group comparison data using neural networks
We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data. Challenges arise in practice. As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios. Worse yet, we have no prior knowledge on the underlying model for a given scenario. These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances. To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common. This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand. Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms. It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets. Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.
This paper attempts to solve match prediction problem, i.e., whether a group is preferred over the other. The key challenge is *consistency* since it*s hard to find the universal pattern over tasks. Instead, this paper propose to learn reward and penalty modules and both vary when the underlying model changes. Experiment results show that the proposed method consistently works the best. My comments: [1] The paper is well written [2] This paper tries to solve an interesting problem but the application is a bit limited [3] It would be great to conduct ablation study, e.g., analyze efforts of different components (R, P, G)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Match prediction from group comparison data using neural networks
We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data. Challenges arise in practice. As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios. Worse yet, we have no prior knowledge on the underlying model for a given scenario. These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances. To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common. This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand. Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms. It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets. Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper attempts to solve match prediction problem, i.e., whether a group is preferred over the other. The key challenge is *consistency* since it*s hard to find the universal pattern over tasks. Instead, this paper propose to learn reward and penalty modules and both vary when the underlying model changes. Experiment results show that the proposed method consistently works the best. My comments: [1] The paper is well written [2] This paper tries to solve an interesting problem but the application is a bit limited [3] It would be great to conduct ablation study, e.g., analyze efforts of different components (R, P, G)
assistant
This paper attempts to solve match prediction problem, i.e., whether a group is preferred over the other. The key challenge is *consistency* since it*s hard to find the universal pattern over tasks. Instead, this paper propose to learn reward and penalty modules and both vary when the underlying model changes. Experiment results show that the proposed method consistently works the best. My comments: [1] The paper is well written [2] This paper tries to solve an interesting problem but the application is a bit limited [3] It would be great to conduct ablation study, e.g., analyze efforts of different components (R, P, G)