November 2017 Summaries

The blog post details the process of using Vespa, an open-source search engine, to create a search and recommendation system for WordPress blog posts. It explains the initial setup of a basic search application using a dataset from a Kaggle challenge, which includes blog posts and user interactions, such as likes. The post covers configuring the Vespa application, indexing the dataset, and setting up search functionalities like sorting, grouping, and ranking based on relevance and custom criteria. It also discusses the importance of attributes for sorting and grouping and their memory usage implications. The blog post concludes with a hint at future enhancements involving machine learning to transform the search application into a recommendation engine.

A Vespa meetup was scheduled on December 4th, 2017, at the Oath/Yahoo Sunnyvale Campus, offering attendees the chance to learn about the Vespa big data serving engine. The event featured a series of presentations by Vespa developers from Norway, covering topics such as tips and tricks, the application of tensors in Vespa, and the future roadmap of the engine. The meetup aimed to provide a platform for sharing experiences, gaining insights, and fostering discussions that could influence the development of Vespa, while also serving as an opportunity for the Vespa team to connect with users. Registration was mandatory, and the event promised to be a valuable experience for attendees interested in big data technologies.