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Article summary:
1. Traditional recommender systems rely on user feedback such as ratings or clicks to the items, which are limited in that they do not tell why users like or dislike an item.
2. A novel dialog-based recommender system is proposed to interactively recommend a list of items with visual appearance and collect natural language based feedback from the user.
3. A variant of the cascading bandits is proposed, where neural representations of the item images and user feedback in natural language are utilized.
Article analysis:
The article provides a detailed overview of a novel dialog-based recommender system for interactive recommendations with visual appearance and collects natural language based feedback from the user. The authors propose a variant of the cascading bandits, where neural representations of the item images and user feedback in natural language are utilized. The article does not present any counterarguments or explore any potential risks associated with this approach, nor does it provide evidence for its claims or discuss any possible biases or one-sided reporting. Additionally, there is no discussion about how partiality may be avoided when collecting user feedback in natural language, nor is there any mention of how to ensure that both sides are presented equally when providing recommendations. Furthermore, there is no mention of how to ensure that promotional content is avoided when providing recommendations. In conclusion, while this article provides an interesting overview of a novel dialog-based recommender system for interactive recommendations with visual appearance, more research needs to be done to address potential biases and risks associated with this approach before it can be considered reliable and trustworthy.