12/9/2023 0 Comments Vectr postANN techniques speed up the search by preprocessing the data into an efficient index, and are often tackled using data structures like inverted files, trees and neighborhood graphs. Approximate Nearest Neighbor (ANN) sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale.ĮBay's Similarity Engine uses an ANN algorithm called Hierarchical Navigable Small World Graphs (HNSW) and Scalable Nearest Neighbors (ScaNN). Traditional nearest neighbor algorithms, like k-Nearest Neighbor Algorithm (kNN), are prohibitively computationally expensive and are not suitable for applications where a low latency response is needed. Items that are close to each other in the embedding space are more similar to each other. These vector representations are often called embeddings. The paradigm for building semantic matching systems is computing vector representations of the entities. Vector-based similarity for deep learning (DL)models tackles these and other keyword-based retrieval pitfalls. In addition, there are many users who are unfamiliar with the jargon of a particular category, or users who do exploratory searches to figure out what their next, more important search will be. Among those are words that have a dual meaning (otherwise known as homonyms) more targeted keyword phrases in which users add a great deal of detail (long tail queries) and user intent. Keyword-based retrieval methods often struggle with certain challenges. ![]() This article takes a look at the architecture we constructed for vector-based similarity, meeting the scale using data sharding, partitioning and replication, and features including attribute-based features and a pluggable ANN backend based on index building, recall accuracy, latencies and memory footprint. More specifically, given an input listing, the similarity engine finds the most similar listings based on listing attributes (title, aspect, image) for item-to-item similarity or generates personalized listing recommendations based on a user’s past browsing activity for user-to-item objective. Recently, the eBay CoreAI team launched an "Approximate Nearest Neighbor" (ANN) vector similarity engine that provides tooling to build use cases that match semantically similar items and personalize recommendations. ![]() These are vital to the shopping experience, and so it’s equally vital that we continuously improve accuracy and robustness in scalability and performance.ĮBay has approximately 134 million users and 1.7 billion live listings at any given time on the marketplace, as of December 2022. Often, ecommerce marketplaces provide buyers with listings similar to those previously visited by the buyer, as well as a personalized shopping experience based on profiles, past shopping histories and behavior signals such as clicks, views and additions to cart.
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