The Travel Recommendation Dataset is a comprehensive dataset designed for building and evaluating conversational recommendation systems in the travel domain. It includes detailed information about users, destinations, and ratings, enabling researchers and developers to create personalized travel recommendation models. The dataset supports use cases such as personalizing travel recommendations, analyzing user behavior, and training machine learning models for recommendation tasks.
This dataset contains detailed technical specifications and market pricing information for power electronic components, specifically MOSFETs and IGBTs from two manufacturers: Wolfspeed and Hitachi. The data was collected through two main methods: manual upload of technical specifications in XML format from the manufacturers' official websites, and real-time market price aggregation using web scraping techniques from the Octopart platform, facilitated by Nexar APIs.
JavaScript Object Notation (JSON) and eXtensible Markup Language (XML) are two data serialisation methods that have been compared over many applications including client-server transmission, internet communication, and large-scale data storage. Due to the smaller file size, JSON is faster for transmitting data. However, XML is better for sending complex data structures. This dataport contains C code project to compare performance of XML with JSON, considering factors such as time, memory, and power to identify efficient characteristics of each method.
