Predictive search, autocomplete, suggested queries, and AI | Algolia

Artificial intelligence (AI) and predictive search have significantly improved user experience in various sectors, including ecommerce and media. Advanced natural language processing is used to enhance sales through AI technologies embedded in these sites. Google's BERT model helps deliver meaningful results for queries that users haven't previously typed. Predictive search, such as autocomplete, offers query suggestions to help users formulate the best query to find desired products or information. This technology anticipates search terms based on user behavior, previous searches, geolocation, and trending searches across all user sessions. Autocomplete, autosuggest, and predictive search are often used interchangeably in the tech industry. The algorithms behind these suggestions begin with simple string matching from static lists or existing data and get increasingly complex by using natural language processing to manage typos and synonyms. Instant search is a cousin of predictive search that displays results in near real-time as users type their queries, improving the search experience by driving discovery visually. Radix trees are used for prefix search, which helps retrieve results as someone types each character. This approach also helps with alternate spellings and typos. The relevance, structure, and design of the search suggestions are critical for user interaction and overall user experience. Predictive search increases conversion rates and saves time for users when searching for an item by providing helpful auto suggestions. It's an important functionality when shopping from a mobile phone with a compact keyboard.