Results and page concepts - Lucidworks documentation

This topic provides information about page components you can manage to improve query results in Predictive Merchandiser. Hierarchical facets allow faster navigation through facet submenus and breadcrumbs. A “parent” facet specified as a general category can contain multiple “children” and “grandchildren” levels that are related. A user can click to open a facet that contains children, and then click a specific facet to bypass the results from the parent to display only the facet selected. In the following example for a clothing site, women is the parent. The children are pants, shirts (and its children), sports bras, coats & jackets, and shorts.

Hierarchical facets are split according to the delimiter set in the Apply Rules query pipeline stage. For more information, see Use Hierarchical Facets.

Use Hierarchical Facets

Product grouping

Product grouping lets you group products in different variations such as color, version, or size. Options to group products include:

Block or boost. For example, to promote a product group that is on sale, boost the group to the top of the results.
Apply rules individually to products within a group. For example, boost the red color version of a product to the top of the group if a specific user typically searches for red.

Best practices

These parameters group products, and are defined in the Additional Query Parameters Stage:

Parameter Name	Parameter Value	Update Policy
group	true	default
group.format	grouped	default
group.ngroups	true	default
group.field	style_id_s	default

For more information about adding a query pipeline and additional query parameters, see:

Add a Query Pipeline Stage
Additional Query Parameters Stage

Add a Query Pipeline Stage

Collapse/Expand parser grouping

The Collapse/Expand parser allows grouping with the following parameters:

Parameter Name	Parameter Value
expand	true
enableElevation	true
group	false

See Collapse and Expand Results for more information.

Page metadata

Metadata describe the content of a web page. The Page Metadata feature in Predictive Merchandiser lets you configure metadata using Use Predictive Merchandiser templates.

Use Predictive Merchandiser templates

When you trigger Predictive Merchandiser templates, the page metadata is added to the response payload, which looks like this:

{
    "triggered": {
        "app": "test",
        "id": "5d9cd572-5928-4a16-a9de-5bab582538a2",
        "name": "example-template",
...
            "name": "Metadata of example-template",
...
            "inTemplates": ["5d9cd572-5928-4a16-a9de-5bab582538a2"],
            "pageMetadata": [{
                "metadata": {
                    "pageTitle": "Example page title",
                    "pageDescription": "Example description",
                    "pageKeywords": ["example"],
                    "imageUrl": null,
                    "pageUrl": null
                }
        }],
        "neverPublished": false,
        "staging": true,
        "published": false
    }
}

The Page Metadata feature also lets you filter your facets by existing tags, clone the values of your facets, or select filters/facets with predefined metadata. If you have a large number of pages, you can also search your domain by Path URL using the URL Search.

For more information, see Use page metadata.

Use page metadata

Search rewrites

Search rewriting lets you modify search queries to more accurately reflect the intentions of your customers. These functions let you:

Create search rewrites manually.
Edit, test, review and publish the search rewrites generated automatically from signals data.

For example, create a misspelling search rewrite to correct common spelling mistakes entered for a particular product. The misspelled query will then produce the same results as a correctly spelled search. Predictive Manager provides four rewrite types:

Use Predictive Merchandiser Query Rewrites - improves poorly performing searches.
Use Predictive Merchandiser Query Rewrites - corrects common spelling mistakes.
Use Predictive Merchandiser Query Rewrites - identifies products with matching phrases.
Use Predictive Merchandiser Query Rewrites - includes alternative words with the same meaning.

Use Predictive Merchandiser Query Rewrites

You can create query rewrite rules in Predictive Merchandiser. This is similar to using the Rules Editor to create query rewrite rules, except there are additional options in Predictive Merchandiser.

In Fusion, navigate to Relevance > Rules > Merchandiser.
Enter a search term or phrase in the search bar.
Click Start Task.
Hover your cursor next to the query. A + button appears:
Click the + button. A list of query rewrites options will appear: Head/Tail, Misspelling, Phrase, Synonym, and Remove Words.

Head/Tail

You can create a Head/Tail rewrite to improve search results using methods other than correcting for misspellings or synonym expansion. When a poorly defined search term is identified, the original term is replaced by an improved search term.For example, a search for iphone case yellow could be improved by using the search term iphone case yellow +color:"yellow" (in this case making use of the color field in the data).Most Head/Tail rewrites are typically created automatically via machine learning. However, if desired, custom rewrites can be manually created using the following steps.

From the list of query rewrite options, select Head/Tail. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Tail Query	The tail query itself.	`iphone case yellow`
Improved Query	The query that will replace the tail query phrase.	`iphone case yellow +color:"yellow"`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more improved search terms in the Improved Query field.
Click the Save button.

The search is re-run using the improved search term, and a Search box information panel drops down to show that the new query rewrite has fired:

Misspelling

You can create a misspelling query rewrite to detect and correct common spelling mistakes. When a customer enters a search term containing a known misspelling, the incorrect spelling is replaced with the spelling correction.For example, if your customers frequently misspell or mistype the word sweater as sweatre, you can set up a query rewrite to automatically correct it.

From the list of query rewrite options, select Misspelling. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Misspelled Term	The phrase itself.	`sweatre`
Corrected Term	The term that will replace the misspelled term.	`sweater`
Action	Action to perform.
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more spelling corrections in the Corrected Term field.
Click the Save button.

The search is re-run using the corrected spelling, and a Search box information panel drops down to show that the new query rewrite has fired:

Phrase

You can use query rewriting to identify phrases used in search terms so that products with matching phrases are boosted in the search results. This is helpful when users do not use quotation marks to identify phrases in their search terms.For example, without phrase detection a search for the words remote control would show results for both remote and control. With phrase detection, this search would correctly boost results for "remote control".

From the list of query rewrite options, select Phrase. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The phrase itself.	`remote control`
Word Count	Indicates how many words are included in the phrase.	`2`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Phrase Count	Denotes how many times this phrase was found in the source. This value is automatically set via machine learning. It does not need to be set manually.	`5`
Boost Factor	The factor to use to boost this phrase in matching queries.	`2.0`
Slop Factor	Phrase slop, or the distance between the terms of the query while still considering it a phrase match.	`10`

Enter the number of words in the phrase in the Word Count field.
Click the Save button.

The search is re-run, with your search words identified as a phrase, and a Search box information panel drops down to show that the new query rewrite has fired:

Synonym

You can specify synonyms for a specified search term so that alternative words with the same meaning are automatically used in the search query. When a customer enters a search term with a synonym match, the alternative words are used instead of, or in addition to, the original search term.For example, the search term sweater could have the synonyms pullover and jumper.

From the list of query rewrite options, select Synonym. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The term that has synonyms.	`sweater`
Direction	With a oneway search, the original search term is replaced by the synonym. In the example above, `sweater` would be replaced by the alternative words `pullover` and `jumper`. With a symmetric search, the search query is expanded to include the original term and the synonyms, resulting in a greater number of potential hits. In the example above, this time the query would include `sweater`, `pullover`, and `jumper`.	`symmetric`
Synonym Mappings	Synonyms for the surface form.	`pullover`, `jumper`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Count	How many times this term occurred in the signal data when it was discovered. This value is optional when a rewrite is being defined manually.	`5`

Choose whether the direction is oneway or symmetric.
Enter one or more alternative words in the Synonym Mappings field.
Click the Save button.

The search is re-run using the synonyms, and a Search box information panel drops down to show that the new query rewrite has fired:

Remove Words

The Remove Words feature is available in Fusion 5.4 and later.

You can create a Remove Words query rewrite to remove words from a query. This query rewrite is helpful when a word in the search query does not add value to the search results.For example, you can rewrite a search query for case study examples to remove examples and then display results for case study.

From the list of query rewrite options, select Remove Words. A form appears:

This form contains the following fields:


Parameter	Description	Example Value
Phrase to remove	The words to remove from the trigger phrase.	`examples`
Trigger phrases	The query that prompts the removal of the phrase. The trigger phrase is not necessarily a complete query. If the query contains the trigger phrase, then Fusion removes the phrase in the Phrase to Remove field.	`case study examples`

Enter a phrase to remove and a trigger phrase. Note that the phrase to remove is auto-populated with the query.
Click Save.

The search is re-run, and a Search box information panel drops down to show that the new query rewrite has fired:

Additional resources

LucidAcademyLucidworks offers free training to help you get started.The Course for Predictive Merchandiser: Rewrites Manager focuses on using search rewrites to boost conversions and create better customer experiences by improving your relevancy:

Visit the LucidAcademy to see the full training catalog.

Use Predictive Merchandiser Query Rewrites

You can create query rewrite rules in Predictive Merchandiser. This is similar to using the Rules Editor to create query rewrite rules, except there are additional options in Predictive Merchandiser.

In Fusion, navigate to Relevance > Rules > Merchandiser.
Enter a search term or phrase in the search bar.
Click Start Task.
Hover your cursor next to the query. A + button appears:
Click the + button. A list of query rewrites options will appear: Head/Tail, Misspelling, Phrase, Synonym, and Remove Words.

Head/Tail

From the list of query rewrite options, select Head/Tail. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Tail Query	The tail query itself.	`iphone case yellow`
Improved Query	The query that will replace the tail query phrase.	`iphone case yellow +color:"yellow"`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more improved search terms in the Improved Query field.
Click the Save button.

The search is re-run using the improved search term, and a Search box information panel drops down to show that the new query rewrite has fired:

Misspelling

From the list of query rewrite options, select Misspelling. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Misspelled Term	The phrase itself.	`sweatre`
Corrected Term	The term that will replace the misspelled term.	`sweater`
Action	Action to perform.
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more spelling corrections in the Corrected Term field.
Click the Save button.

The search is re-run using the corrected spelling, and a Search box information panel drops down to show that the new query rewrite has fired:

Phrase

From the list of query rewrite options, select Phrase. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The phrase itself.	`remote control`
Word Count	Indicates how many words are included in the phrase.	`2`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Phrase Count	Denotes how many times this phrase was found in the source. This value is automatically set via machine learning. It does not need to be set manually.	`5`
Boost Factor	The factor to use to boost this phrase in matching queries.	`2.0`
Slop Factor	Phrase slop, or the distance between the terms of the query while still considering it a phrase match.	`10`

Enter the number of words in the phrase in the Word Count field.
Click the Save button.

The search is re-run, with your search words identified as a phrase, and a Search box information panel drops down to show that the new query rewrite has fired:

Synonym

From the list of query rewrite options, select Synonym. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The term that has synonyms.	`sweater`
Direction	With a oneway search, the original search term is replaced by the synonym. In the example above, `sweater` would be replaced by the alternative words `pullover` and `jumper`. With a symmetric search, the search query is expanded to include the original term and the synonyms, resulting in a greater number of potential hits. In the example above, this time the query would include `sweater`, `pullover`, and `jumper`.	`symmetric`
Synonym Mappings	Synonyms for the surface form.	`pullover`, `jumper`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Count	How many times this term occurred in the signal data when it was discovered. This value is optional when a rewrite is being defined manually.	`5`

Choose whether the direction is oneway or symmetric.
Enter one or more alternative words in the Synonym Mappings field.
Click the Save button.

The search is re-run using the synonyms, and a Search box information panel drops down to show that the new query rewrite has fired:

Remove Words

The Remove Words feature is available in Fusion 5.4 and later.

From the list of query rewrite options, select Remove Words. A form appears:

This form contains the following fields:


Parameter	Description	Example Value
Phrase to remove	The words to remove from the trigger phrase.	`examples`
Trigger phrases	The query that prompts the removal of the phrase. The trigger phrase is not necessarily a complete query. If the query contains the trigger phrase, then Fusion removes the phrase in the Phrase to Remove field.	`case study examples`

Enter a phrase to remove and a trigger phrase. Note that the phrase to remove is auto-populated with the query.
Click Save.

The search is re-run, and a Search box information panel drops down to show that the new query rewrite has fired:

Additional resources

Visit the LucidAcademy to see the full training catalog.

Use Predictive Merchandiser Query Rewrites

You can create query rewrite rules in Predictive Merchandiser. This is similar to using the Rules Editor to create query rewrite rules, except there are additional options in Predictive Merchandiser.

In Fusion, navigate to Relevance > Rules > Merchandiser.
Enter a search term or phrase in the search bar.
Click Start Task.
Hover your cursor next to the query. A + button appears:
Click the + button. A list of query rewrites options will appear: Head/Tail, Misspelling, Phrase, Synonym, and Remove Words.

Head/Tail

From the list of query rewrite options, select Head/Tail. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Tail Query	The tail query itself.	`iphone case yellow`
Improved Query	The query that will replace the tail query phrase.	`iphone case yellow +color:"yellow"`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more improved search terms in the Improved Query field.
Click the Save button.

The search is re-run using the improved search term, and a Search box information panel drops down to show that the new query rewrite has fired:

Misspelling

From the list of query rewrite options, select Misspelling. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Misspelled Term	The phrase itself.	`sweatre`
Corrected Term	The term that will replace the misspelled term.	`sweater`
Action	Action to perform.
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more spelling corrections in the Corrected Term field.
Click the Save button.

The search is re-run using the corrected spelling, and a Search box information panel drops down to show that the new query rewrite has fired:

Phrase

From the list of query rewrite options, select Phrase. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The phrase itself.	`remote control`
Word Count	Indicates how many words are included in the phrase.	`2`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Phrase Count	Denotes how many times this phrase was found in the source. This value is automatically set via machine learning. It does not need to be set manually.	`5`
Boost Factor	The factor to use to boost this phrase in matching queries.	`2.0`
Slop Factor	Phrase slop, or the distance between the terms of the query while still considering it a phrase match.	`10`

Enter the number of words in the phrase in the Word Count field.
Click the Save button.

The search is re-run, with your search words identified as a phrase, and a Search box information panel drops down to show that the new query rewrite has fired:

Synonym

From the list of query rewrite options, select Synonym. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The term that has synonyms.	`sweater`
Direction	With a oneway search, the original search term is replaced by the synonym. In the example above, `sweater` would be replaced by the alternative words `pullover` and `jumper`. With a symmetric search, the search query is expanded to include the original term and the synonyms, resulting in a greater number of potential hits. In the example above, this time the query would include `sweater`, `pullover`, and `jumper`.	`symmetric`
Synonym Mappings	Synonyms for the surface form.	`pullover`, `jumper`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Count	How many times this term occurred in the signal data when it was discovered. This value is optional when a rewrite is being defined manually.	`5`

Choose whether the direction is oneway or symmetric.
Enter one or more alternative words in the Synonym Mappings field.
Click the Save button.

The search is re-run using the synonyms, and a Search box information panel drops down to show that the new query rewrite has fired:

Remove Words

The Remove Words feature is available in Fusion 5.4 and later.

From the list of query rewrite options, select Remove Words. A form appears:

This form contains the following fields:


Parameter	Description	Example Value
Phrase to remove	The words to remove from the trigger phrase.	`examples`
Trigger phrases	The query that prompts the removal of the phrase. The trigger phrase is not necessarily a complete query. If the query contains the trigger phrase, then Fusion removes the phrase in the Phrase to Remove field.	`case study examples`

Enter a phrase to remove and a trigger phrase. Note that the phrase to remove is auto-populated with the query.
Click Save.

The search is re-run, and a Search box information panel drops down to show that the new query rewrite has fired:

Additional resources

Visit the LucidAcademy to see the full training catalog.

Use Predictive Merchandiser Query Rewrites

You can create query rewrite rules in Predictive Merchandiser. This is similar to using the Rules Editor to create query rewrite rules, except there are additional options in Predictive Merchandiser.

In Fusion, navigate to Relevance > Rules > Merchandiser.
Enter a search term or phrase in the search bar.
Click Start Task.
Hover your cursor next to the query. A + button appears:
Click the + button. A list of query rewrites options will appear: Head/Tail, Misspelling, Phrase, Synonym, and Remove Words.

Head/Tail

From the list of query rewrite options, select Head/Tail. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Tail Query	The tail query itself.	`iphone case yellow`
Improved Query	The query that will replace the tail query phrase.	`iphone case yellow +color:"yellow"`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more improved search terms in the Improved Query field.
Click the Save button.

The search is re-run using the improved search term, and a Search box information panel drops down to show that the new query rewrite has fired:

Misspelling

From the list of query rewrite options, select Misspelling. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Misspelled Term	The phrase itself.	`sweatre`
Corrected Term	The term that will replace the misspelled term.	`sweater`
Action	Action to perform.
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`

Enter one or more spelling corrections in the Corrected Term field.
Click the Save button.

The search is re-run using the corrected spelling, and a Search box information panel drops down to show that the new query rewrite has fired:

Phrase

From the list of query rewrite options, select Phrase. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The phrase itself.	`remote control`
Word Count	Indicates how many words are included in the phrase.	`2`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Phrase Count	Denotes how many times this phrase was found in the source. This value is automatically set via machine learning. It does not need to be set manually.	`5`
Boost Factor	The factor to use to boost this phrase in matching queries.	`2.0`
Slop Factor	Phrase slop, or the distance between the terms of the query while still considering it a phrase match.	`10`

Enter the number of words in the phrase in the Word Count field.
Click the Save button.

The search is re-run, with your search words identified as a phrase, and a Search box information panel drops down to show that the new query rewrite has fired:

Synonym

From the list of query rewrite options, select Synonym. A form will appear:

This form contains the following fields:


Parameter	Description	Example Value
Surface Form	The term that has synonyms.	`sweater`
Direction	With a oneway search, the original search term is replaced by the synonym. In the example above, `sweater` would be replaced by the alternative words `pullover` and `jumper`. With a symmetric search, the search query is expanded to include the original term and the synonyms, resulting in a greater number of potential hits. In the example above, this time the query would include `sweater`, `pullover`, and `jumper`.	`symmetric`
Synonym Mappings	Synonyms for the surface form.	`pullover`, `jumper`
Confidence	Confidence score from the phrase job. A confidence level of `1` represents 100% confidence. For rules created automatically via machine learning, the confidence level will reflect the output from the machine learning model.	`1`
Tags	Optional metadata tags that can be used to identify and organize rewrites.	`blackfridaysale`
Count	How many times this term occurred in the signal data when it was discovered. This value is optional when a rewrite is being defined manually.	`5`

Choose whether the direction is oneway or symmetric.
Enter one or more alternative words in the Synonym Mappings field.
Click the Save button.

The search is re-run using the synonyms, and a Search box information panel drops down to show that the new query rewrite has fired:

Remove Words

The Remove Words feature is available in Fusion 5.4 and later.

From the list of query rewrite options, select Remove Words. A form appears:

This form contains the following fields:


Parameter	Description	Example Value
Phrase to remove	The words to remove from the trigger phrase.	`examples`
Trigger phrases	The query that prompts the removal of the phrase. The trigger phrase is not necessarily a complete query. If the query contains the trigger phrase, then Fusion removes the phrase in the Phrase to Remove field.	`case study examples`

Enter a phrase to remove and a trigger phrase. Note that the phrase to remove is auto-populated with the query.
Click Save.

The search is re-run, and a Search box information panel drops down to show that the new query rewrite has fired:

Additional resources

Visit the LucidAcademy to see the full training catalog.

Merchandise zero results

Search queries that return zero results can drive customers away from your site. You can set certain products to display when a customer search generates zero results. For more information, see Merchandising Zero Results.

Merchandising Zero Results

Rule conflict resolution

Sometimes, rules created in the Rules Editor and Predictive Merchandiser conflict with each other. For example, if you have three rules to boost, bury, and block the same product, Predictive Merchandiser highlights these conflicting rules so you can address them. To highlight conflicting rules, the Rules Fired section displays a red warning icon next to the dominant rule and a red flag on the conflicting rules.

Click the Edit button to view the details of the conflicting rules and resolve the conflict.

The details of the conflicting rules affecting specific products are found in the details panel. Click the Start Task button

, hover over a product, and click the Details button

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Hierarchical facets

Product grouping

Best practices

Collapse/Expand parser grouping

Page metadata

Search rewrites

Merchandise zero results

Rule conflict resolution