Learning to Rank with Vespa – Getting started with Text Search

Vespa.ai has published two tutorials designed to help users get started with text search applications by leveraging the capabilities of Vespa, a platform for building scalable search solutions. Based on Microsoft's MS MARCO dataset, the first tutorial guides users through creating a basic text search application, deploying it with Vespa, and experimenting with various ranking functions like nativeRank and BM25. The second tutorial focuses on generating training datasets to enhance app ranking functions, emphasizing the importance of listwise loss functions over pointwise ones for optimizing Mean Reciprocal Rank (MRR), using frameworks like TF-Ranking. The tutorials provide detailed instructions and code to facilitate the reproduction of steps, encouraging users to experiment with different ranking profiles and improve search result relevance.

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