Jun XieSemantic Search ETLETL for semantic search (extract, transform and load) aims to retrieve data from different systems, transform the raw data (e.g., text…2 min read·Oct 6, 2022----
Jun XieTop Pre-trained Models for Sentence EmbeddingThis article walks through top pre-trained models to get sentence embedding, which is a lower-dimensional numerical representation of the…2 min read·Oct 3, 2022----
Jun XieWeb-Scale Recommendation SystemsThis article walks through a tutorial on web-scale recommendation systems, which are normally built in the largest Internet companies…3 min read·Oct 3, 2022----
Jun XieSemantic search on billion-level documentsSharding is needed to enable semantic search on billion-level documents. If the semantic search traffic is high, we also need to do the…2 min read·Sep 13, 2022----
Jun XieTop 5 Pre-trained Model for Image EmbeddingPre-trained models help boost the popularity of semantic search. We can easily get embedding (aka. vector) of different media (i.e. text…2 min read·Sep 1, 2022----
Jun XieSemantic Search KNNSemantic search uses K-nearest neighbor search (KNN) to return the most relevant results given a query (i.e. vector + metadata filtering)…1 min read·Aug 30, 2022----
Jun XieSemantic Search ANNApproximate nearest neighbor search (ANN) makes semantic search possible and blazing fast on billions of documents. ANN is also called…3 min read·Aug 30, 2022----
Jun XieSemantic Search ApplicationsSemantic search powers more and more industrial applications, including recommendation, and document/image/audio/video retrieval.2 min read·Aug 27, 2022----
Jun XieSemantic Search Similarity MetricsThere are three common similarity metrics used in the semantic search to measure the similarity/distance between vectors, including squared…2 min read·Aug 26, 2022----
Jun XieRelated Product RecommendationThis article walks through a general architecture to support the related/similar product recommendation based on semantic search.2 min read·Aug 25, 2022----