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Fusion 5.12
    Fusion 5.12

    Query Enhancement

    Query enhancement refers to features that improve search results by analyzing query intent, correcting malformed queries, boosting popular items, recommending items from related queries, and so on.

    These topics show you how to use the basic query enhancement features:

    • 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.

    • 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.

    Many of these are examples of query rewriting.

    See also Advanced Query Enhancement for even more sophisticated features designed for implementation by data scientists.