The explosion of conference paper submissions in AI and related fields, has underscored the need to improve many aspects of the peer review process, especially the matching of papers and reviewers. Recent work argues that the key to improve this matching is to modify aspects of the \emph{bidding phase} itself, to ensure that the set of bids over papers is balanced, and in particular to avoid \emph{orphan papers}, i.e., those papers that receive no bids. In an attempt to understand and mitigate this problem, we have developed a flexible bidding platform to test adaptations to the bidding process. Using this platform, we performed a field experiment during the bidding phase of a medium-size international workshop that compared two bidding methods. We further examined via controlled experiments on Amazon Mechanical Turk various factors that affect bidding, in particular the order in which papers are presented \cite{cabanac2013capitalizing,fiez2020super}; and information on paper demand \cite{meir2021market}. Our results suggest that several simple adaptations, that can be added to any existing platform, may significantly reduce the skew in bids, thereby improving the allocation for both reviewers and conference organizers.

As a junior data scientist, most of the times training data are ready for me to train the model (either by accessing database/external csv file provided). However, dataset is not always available for us, we need scraping skill to make our life easier. My friend and I decided to start this mini-project which contains the whole process from scraping, to exploratory and finally perform some simple prediction using various machine learning models within 1 day as a small challenge for our own. We can't wait to share our findings and hopefully you could enjoy reading it. Besides, feel free to reach us by commenting below if you find anything unclear/spot some points we have miss out!:) We will be scrapping TOP 100 COOLEST TECH GADGETS OF 2018 from bestproducts.com