Summary of Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking, by Dipika Jha et al.
Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking
by Dipika Jha, Ankit K. Bhagat, Raju Halder, Rajendra N. Paramanik, Chandra M. Kumar
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents IITP-VDLand, a comprehensive Decentraland parcels dataset sourced from various platforms. Unlike existing datasets, IITP-VDLand offers a rich array of attributes, including parcel characteristics, trading history, past activities, transactions, and social media interactions. The dataset also includes a Rarity score, measuring the uniqueness of each parcel within the virtual world. To gather the data, the authors employed a systematic approach using APIs and custom scripts, curating and organizing it into four fragments: Characteristics, OpenSea Trading History, Ethereum Activity Transactions, and Social Media. The dataset is envisioned as a robust resource for training machine- and deep-learning models to address real-world challenges in Decentraland parcels. Benchmarking over 20 state-of-the-art price prediction models on the dataset yields promising results, with ensemble models performing better than deep learning and linear models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big database of special virtual land pieces called “parcels” from different places online. It’s like a big book that helps computers learn about these parcels and how they change value over time. The book has lots of information, including what each parcel is like, who bought or sold it before, and even things people posted on social media about it. This database can help make better computers that can predict how valuable these parcels will be in the future. |
Keywords
» Artificial intelligence » Deep learning
