the semantic gap between user query and documents. > The key points: semantic features and ranking function. > Search is a ranking problem. The ordering is more important than the predicted probability of a single instance.
representations which obtains state-of-the-art results on a wide array of NLP tasks. Source: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Bidirectional Encoder Representations from Transformers
models for information retrieval systems > Caring more about ranking rather than rating prediction > Scoring by machine learning • Creating document index by Elasticsearch • Using embeddings to train ranking models • Serving search queries by Elasticsearch with ranking models