Query fan out refers to the automated generation and submission of multiple variants of a user's original search query to a search system. Using a trained generative model—typically a neural network such as an LSTM or GRU-based sequence-to-sequence model—this process aims to increase the likelihood of retrieving relevant information by exploring different formulations and interpretations of the user's intent.
How Query Fan Out Works
1. Receiving the Original Query
- A user submits a search query via a client device (e.g., computer, smartphone).
- The query is received in textual form, or converted to text if necessary.
2. Generating Query Variants
- The generative model processes the original query, often along with additional input features (such as user location, current time, or predicted user task).
- The model generates multiple variants of the query. These can include:
- Equivalent queries (different wording, same meaning)
- Follow-up or clarification queries
- Generalization or specification queries
- Canonicalized or language-translated versions
- Each variant is designed to represent a possible reinterpretation or extension of the user’s original intent.
Example
| Original Query | Generated Variants |
|---|---|
| "funny cat pictures" | "funny cat pictures with captions", "funny kitten images", "pictures of cats doing funny things" |
3. Submitting Variants to the Search System
- Each generated variant is automatically submitted to the search system, often in parallel.
- The search system returns results or answers for each variant.
4. Analyzing Responses
- The query system evaluates the responses to each variant.
- The best or most relevant response(s) are selected, possibly using response scores or corroboration among multiple answers.
- In cases where no satisfactory results are found, the system may prompt the user with a clarification question or suggest alternative queries.
5. Providing Output to the User
- The system presents the best answer(s), suggested query variants, or clarification prompts to the user.
- This can improve user experience by reducing the need to manually reformulate queries and by surfacing more accurate or complete search results.
Benefits of Query Fan Out
- Improved Relevance: Increases the likelihood of retrieving useful results, especially for rare or ambiguous queries.
- Personalization: Variants can be tailored based on user attributes, context, or predicted tasks.
- Accessibility: Helps users who have difficulty typing or formulating queries by automatically suggesting alternative phrasings.
- Efficiency: Reduces the need for users to re-enter or refine queries manually.
Technical Details
- The generative model is often a sequence-to-sequence neural network with memory layers (e.g., LSTM, GRU).
- Additional features—such as user location, time, or task—can be fed into the model for context-aware variant generation.
- A control model may be used to dynamically determine how many variants to generate and when to stop, optimizing efficiency and relevance.
- Both generative and control models may be trained with reinforcement learning, using feedback from search system responses as rewards.
For more technical information, see US Patent US11663201B2 - Generating query variants using a trained generative model.