- Signals are the foundation of many query enhancement strategies. Enabling and collecting signals allows you to enhance query results based on the past behavior of your users.
- Each Fusion app comes with a basic list of stopwords that you can modify to suit your use case.
- Boosting can be done manually or using signals to boost the most desirable items to the top of search results.
- Synonyms are alternative query terms used to expand the original query.
- Business rules are a versatile strategy for manually rewriting queries to address a wide range of use cases.
- Collapsing results is a feature that groups all variations of each item into a single search result. This is useful for e-commerce sites where you want to show a single product with multiple SKUs or variations.
- Misspelling detection automatically rewrites misspelled queries to return relevant results.
- Phrase detection automatically finds phrases so that results with matching phrases can be boosted.
- Response rewriting modifies Solr’s response, rather than the user’s query.
- The underperforming queries feature identifies queries that can be rewritten to obtain more relevant results.
- Recommendations can be signals-based or content-based, and items are recommended based on the current item, user, or query.
- Predictive Merchandiser is a visual tool for manipulating search results to present the best items to e-commerce shoppers.
- Experience Optimizer, similar to Predictive Merchandiser, is a visual tool for curating search results in a knowledge management environment.
Using Advanced Query Tools in Fusion
The course for Using Advanced Query Tools in Fusion focuses on how to apply parameters, LocalParams, function queries, and query re-ranking using the Fusion UI.
Query Fine-Tuning
The learning path for Query Fine-Tuning focuses on best practices in tuning query relevancy to get the best results out of Fusion.