Meta Decision Trees for Explainable Recommendation Systems
We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user’s training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature. Our code is attached as supplementary.
This paper presents a user-specific decision tree method for building explainable recommendation systems. A regression function is built to map training samples to a decision rule at each node and a second network is similarly developed to learn the value at the leaf nodes. They are trained end-to-end with a data loss on the target values with a sparsity term. Experiments show competitive performance which however is worse than the latest state of the art results. Pros: Generally, the paper is technically justified and clearly written. Cons: 1- A further discussion is needed about the exact advantage of the proposed regression networks in comparison with the similar works. 2- The experimental comparison seems to be insufficient. Only four state-of-the-art methods are compared and in table 1 about the missing results, a reasonable explanation is needed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Meta Decision Trees for Explainable Recommendation Systems
We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user’s training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature. Our code is attached as supplementary.
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 presents a user-specific decision tree method for building explainable recommendation systems. A regression function is built to map training samples to a decision rule at each node and a second network is similarly developed to learn the value at the leaf nodes. They are trained end-to-end with a data loss on the target values with a sparsity term. Experiments show competitive performance which however is worse than the latest state of the art results. Pros: Generally, the paper is technically justified and clearly written. Cons: 1- A further discussion is needed about the exact advantage of the proposed regression networks in comparison with the similar works. 2- The experimental comparison seems to be insufficient. Only four state-of-the-art methods are compared and in table 1 about the missing results, a reasonable explanation is needed.
assistant
This paper presents a user-specific decision tree method for building explainable recommendation systems. A regression function is built to map training samples to a decision rule at each node and a second network is similarly developed to learn the value at the leaf nodes. They are trained end-to-end with a data loss on the target values with a sparsity term. Experiments show competitive performance which however is worse than the latest state of the art results. Pros: Generally, the paper is technically justified and clearly written. Cons: 1- A further discussion is needed about the exact advantage of the proposed regression networks in comparison with the similar works. 2- The experimental comparison seems to be insufficient. Only four state-of-the-art methods are compared and in table 1 about the missing results, a reasonable explanation is needed.