January 2021 Summaries

Tokopedia has introduced similarity search to improve the relevance of its product search results. The platform uses Elasticsearch for keyword-based search, which ranks products based on their frequency and proximity in a document. To enhance meaning comparison, they adopted vector representation, encoding words by their probable context. Milvus was chosen as the feature vector search engine due to its ease of use and support for more indexes. The platform deployed one writable node, two read-only nodes, and one Mishards middleware instance in Google Cloud Platform (GCP) using Milvus Ansible. Indexing plays a crucial role in accelerating similarity searches on large datasets by organizing data efficiently. Tokopedia plans to improve the model's performance for obtaining embeddings and run multiple learning models simultaneously for future experiments like image search and video search.

Artificial intelligence (AI) has the potential to revolutionize various industries and tasks. One example is race timing, where AI can replace traditional chip timers with video cameras and machine learning algorithms. This technology, known as vector similarity search, involves converting unstructured data into feature vectors using neural networks, then calculating similarities between these vectors. Vector similarity search has applications in e-commerce, security, recommendation engines, chatbots, image or video search, and chemical similarity search. Open-source software like Milvus and publicly available datasets make AI more accessible to developers and businesses.