Tuesday, July 14, 2020

How to enable Did you mean feature in Adobe Search and Promote | Enable Guided Suggestions in Adobe Search and Promote

This tutorial explains the details on “Did You Mean” feature in Adobe Search and Promote.

You can configure “Did You Mean” so that customers are given suggestions for valid search terms when they have tried searches that have failed. Suggestions are formed by looking for spelling and typing corrections to the search terms that result in a valid search.

This feature helps to avoid the null search due to wrong spelling of words in the search term and improve the conversion.

Configuring Did You Mean

You can tailor how site search/merchandising makes search suggestions when a customer’s query returns no, or minimal, search results.

To enable Did You Mean feature, access Linguistics → Did You Mean

adobe-search-and-promote-did-you-mean

Remove these Words from Suggestions — enter space or line separated words to filter undesirable suggestions.

Suggestion Algorithm — Adjusts how far the software goes to find suggestions. If a user makes a one-letter mistake, all of the algorithms come up with the same suggestions. The reason why is because it only takes one edit to arrive at a working suggestion, and all algorithms find words that are close to the original. But when the original search terms are not similar to existing terms in the index, the Deep and Bad Spellers Suggestion Algorithms continue to search for possible suggestions. This scenario is useful if a customer tries a proper name that is hard to type, and they sound out the names. However, if you only want similar suggestions to appear, you can choose the Quick algorithm

Default count of suggestions to show — Specifies the number of Did You Mean term suggestions (0–20) that you want to show when a customer’s search returns no results. The default is 3.

Minimal suggestion word length — Prunes Did You Mean terms by specifying the minimal number of letters for a suggested word. For example, if you set the value to 4, the software does not suggest a word that is 1, 2, or 3 characters long. If you specify a value of 0, no short words are removed from the term suggestions. If you specify a high value, it usually results in no term suggestions. The default value is 3.

Minimum index frequency — Specifies the minimum number of times a word has to appear in the index before it is included in the Did You Mean dictionary. You cannot specify a negative number in the field.

Search for suggest term if no results — Automatically re-searches for the first suggested term when a customer’s search yields no results and there is at least one Did You Mean term suggestion.

Make suggestions due to low results — If a customer searches for a term that yields less than ten results, the search engine checks to see if it has a suggestion that can yield more than 100 results. The number of suggestions is controlled by the value that is specified in Default count of suggestions to show. The low and high threshold are configurable by the options below.

Make suggestions when the initial results are fewer than — Controls the number of results when the system starts to offer suggestions. This option appears only when you check Make suggestions due to low results. The default is 10.

The Did You Mean functionality can be enabled in three ways: make suggestions due to no results, automatically search against the first suggestion when we have no results, or make suggestions due to low results (where the suggestions have a higher result count).

Let us enable the indexing with IndexConnector(the URL entrypoint can be used if required), refer the below URL for details on enabling IndexConnector and configuring custom templates.

The sample feed data for indexing, enable the required metadata, IndexConnector, URL entrypoint and the custom templates by following the previous tutorial URL

<feed xmlns:xs="http://www.w3.org/2001/XMLSchema" version="2.0">
<channel>
<title>Product Feed</title>
<Item>
<link>https://qa.example.com/product-title/p/prod1</link>
<title>
<![CDATA[Java Title]]>
</title>
<description>
<![CDATA[<p>Prod1 description</p>]]>
</description>
<productType>Java</productType>
<ProductId>prod1</ProductId>
<imageUrl>/content/dam/Images/product/prod1.jpg</imageUrl>
</Item>
<Item>
<link>https://qa.example.com/product-title/p/prod2</link>
<title>
<![CDATA[Lava Title]]>
</title>
<description>
<![CDATA[<p>Prod2 description</p>]]>
</description>
<productType>Lava</productType>
<ProductId>prod2</ProductId>
<imageUrl>/content/dam/Images/product/prod2.jpg</imageUrl>
</Item>

</channel>
</feed>

Enable the transport template — custom_backend_json.tpl

<search-content-type-header charset="UTF-8">
{
"general": {
"query" : "<search-query />",
"total" : "<search-total />",
"lower" : "<search-lower />",
"upper" : "<search-upper />"
},
<search-if-suggestions>
"suggestions":
[
<search-suggestions>
{
"suggestion":"<search-suggestion-text />",
"count": "<search-suggestion-result-count>"
}<search-if-not-last-suggestion>,</search-if-not-last-suggestion>
</search-suggestions>
],
</search-if-suggestions>


"facets" : [
{
"name" : "n1",
"values" : [<search-field-value-list name="n1" quotes="yes" data="values" sortby="values" encoding="json" />],
"counts" : [<search-field-value-list name="n1" quotes="no" data="results" sortby="values" />]
}
],
"results" : [
<search-results>
{
"fields" :
[
{
"name" : "mdi",
"value" : "<search-display-field name="mdi" length="500" encoding="json" />"
},
{
"name" : "title",
"value" : "<search-display-field name="title" encoding="json" />"
},
{
"name" : "productType",
"value" : "<search-display-field name="productType" encoding="json" />"
}
]
}
<search-if-not-last>,</search-if-not-last>
</search-results>
]
}

Enable the transport template — custom_presentation_json.tmpl

<guided-content-type-header content="application/json" />
<guided-if-query-param-defined gsname="callback" /><guided-query-param gsname="callback" />(</guided-if-query-param-defined>
{
"general" :
{
"query" : "<guided-query-param gsname='q' />",
"total" : "<guided-results-total />",
"page_lower" : "<guided-results-lower>",
"page_upper" : "<guided-results-upper>",
"page_total": "<guided-page-total/>"
},

"suggestions":
{
"auto_searched": "<guided-if-suggestion-autosearch>1<guided-else-suggestion-autosearch>0</guided-if-suggestion-autosearch>"
<guided-if-suggestion-autosearch>,
"auto_search_query":"<guided-query-param gsname="q"/>",
"original_query":"<guided-suggestion-original-query />"
</guided-if-suggestion-autosearch>,
"suggestion_low_result":"<guided-if-suggestion-low-results>1<guided-else-suggestion-low-results>0</guided-if-suggestion-low-results>",
"suggestion_items":[
<guided-if-suggestions>
<guided-suggestions>
{
"path": "<guided-suggestion-path>",
"value": "<guided-suggestion>",
"count": "<guided-suggestion-result-count>"
}<guided-if-not-last>,</guided-if-not-last>
</guided-suggestions>
</guided-if-suggestions>
]
},"facets" :
[

],
"results" :
[
<guided-results gsname="default">
{
"index" : "<guided-result-index />",
"title" : "<guided-result-field gsname="title" escape="ijson" />",
"productType" : "<guided-result-field gsname="productType" escape="ijson" />"
}<guided-if-not-last>,</guided-if-not-last>
</guided-results>
]
}
<guided-if-query-param-defined gsname="callback">)</guided-if-query-param-defined>

The configurations are ready, let us now run a Stage indexing (note the configuration are not pushed to live yet)

Index →Full Index →Staged Index →Run Full Index

Image for post

Let us now test with the default “Did You Mean” configurations

Access http://stage-xxxxxxxxxxx.guided.ss-omtrdc.net/do=json&sp_staged=1&q=bava

xxxxxxxxxxx — Search and Promote account number

There is no matching data for the term “bava”, this will provide all the available suggestions with search link and search value those data can be used to show the suggestion to the users.

{
"general": {
"query": "bava",
"total": "0",
"page_lower": "0",
"page_upper": "0",
"page_total": "1"
},
"suggestions": {
"auto_searched": "0",
"suggestion_low_result": "0",
"suggestion_items": [
{
"path": "?do=json;i=1;q=lava;sp_staged=1",
"value": "lava",
"count": "1"
},
{
"path": "?do=json;i=1;q=java;sp_staged=1",
"value": "java",
"count": "1"
}
]

},
"facets": [],
"results": []
}

Let us now enable “Search for suggest term if no results” option — Automatically re-searches for the first suggested term when a customer’s search yields no results and there is at least one Did You Mean term suggestion.

Image for post

Access http://stage-xxxxxxxxxxx.guided.ss-omtrdc.net/do=json&sp_staged=1&q=bava, this will provide all the available suggestions(other than the first suggestion used for auto search) and returns the result by searching with first suggested term.

{
"general": {
"query": "lava",
"total": "1",
"page_lower": "1",
"page_upper": "1",
"page_total": "1"
},
"suggestions": {
"auto_searched": "1",
"auto_search_query": "lava",
"original_query": "bava",
"suggestion_low_result": "0",
"suggestion_items": [
{
"path": "?do=json;i=1;q=java;sp_staged=1",
"value": "java",
"count": "1"
}
]

},
"facets": [],
"results": [
{
"index": "",
"title": "Lava Title",
"productType": "Lava"
}
]

}

“Make suggestions due to low results” — if a customer searches for a term that yields less than ten results, the search engine checks to see if it has a suggestion that can yield more than 100 results.

Image for post

This will return “suggestion_low_result”: “1” and the available suggestions that yield more than 100 results but somehow the scenario is not working as expected. I will be updating the tutorial with further details.

The configuration can be pushed live after successful validation and run a live index →Full Index →Live Index →Run Full Index

The URL to access live data http://xxxxxxxxxxx.guided.ss-omtrdc.net/do=json&q=bava

The suggest data returned can be used to enable the “Did You Mean” functionality in the website.



Saturday, July 11, 2020

How to configure nested facet in Adobe Search and Promote?

This tutorial explains how to configure nested facet in Adobe Search and Promote.

Nested Facet

Nested facets are facets that display multiple levels of categories as in the following

The Books, Discount and Watches categories are in the top or parent facet. The subcategories, such as Legal, 10% and Tax, are in the lower or child facet.

The current supported nested facet depth is two, but it can be anywhere along the drill-down list.

The data for this facet must follow a convention in describing the two levels of categories. For example, a facet value can be ‘Books:Legal’ where the parent category is ‘Books’ and the child category is ‘Legal’. The ‘:’ is used as a delimiter to separate them.

To generate the data in this format, you can use a filter script to combine two existing categories.

The following are the behaviors of various types of nested facets:

Behavior of nested facet type

Normal

The behavior of a normal nested facet is that it shrinks if other facets narrow the search. Normal If the nested facet is selected, it shrinks down toward its selection. If a parent facet is selected, only that parent appears with all of its remaining children facets. If a child facet is selected, the facet only shows the selected parent facet and the selected child facet.

Sticky

The behavior of a sticky nested facet is that it tries to keep the facet open as much as possible based on the state of other facets Sticky or search criteria. If the child facet is selected, it counts toward the sticky depth

Multi-Select

The behavior of a multi-select facet is that it keeps the facet open. Any new selections try to wipe out all other facet Multi-Select selections unless the facet is a “parent” of the category nested facet. In this case, “parent” refers to category facets, not top-level categories of a nested facet.

Category Multi-Select

Like Multi-Select nested facet type with the following exceptions: Category Multi-Select

  • Any other facets previously chosen are deselected if this facet is selected for the first time.
  • Other facets previously chosen are also deselected if the customer drills straight down to the child facet without clicking the parent facet or a sibling of a different parent facet is chosen.
  • They can have parents in the sense that category facets have parents. Do not confuse this behavior with parent-child relationships found with all nested facets.

Refer the following URL to configure the basic navigation and dynamic facet in Adobe Search and Promote

Keep the following in mind when you create a nested facet:

  • Each nested facet requires one user-defined meta tag field.
  • Nested facets are composed of two other facets, the parent facet and the child facet. They can be single value facets or multi-value facets. The mixing of single-value facets and multi-value facets is not allowed.
  • You need to determine if this facet will be used in the search field table. The field table requires the nested facet itself and its compositing facets.
  • Consider using JSON to implement nested facets; it is easier.

Configure nested facet

As a first step, let us enable the required fields in the IndexConnector data feed(I am using IndexConnector for indexing the data)

product_QA.xml

<feed xmlns:xs="http://www.w3.org/2001/XMLSchema" version="2.0">
<channel>
<title>Product Feed</title>
<Item>
<link>https://qa.example.com/product-title/p/prod1</link>
<title>
<![CDATA[Book Prod1 Title]]>
</title>
<description>
<![CDATA[<p>Prod1 description</p>]]>
</description>
<productType>Book</productType>
<t1>Books|Discount</t1>
<t2>Legal|10%</t2>

<ProductId>prod1</ProductId>
<imageUrl>/content/dam/Images/product/prod1.jpg</imageUrl>
</Item>
<Item>
<link>https://qa.example.com/product-title/p/prod3</link>
<title>
<![CDATA[Book Prod2 Title]]>
</title>
<description>
<![CDATA[<p>Prod2 description</p>]]>
</description>
<productType>Book</productType>
<t1>Books|Discount</t1>
<t2>Tax|10%</t2>
<size>10</size>

<ProductId>prod2</ProductId>
<imageUrl>/content/dam/Images/product/prod3.jpg</imageUrl>
</Item>
<Item>
<link>https://qa.example.com/product-title/p/prod1</link>
<title>
<![CDATA[Watch Prod3 Title]]>
</title>
<description>
<![CDATA[<p>Prod3 description</p>]]>
</description>
<productType>Watch</productType>
<t1>Watches|Discount</t1>
<t2>Gramini|5%</t2>

<ProductId>prod3</ProductId>
<imageUrl>/content/dam/Images/product/prod3.jpg</imageUrl>
</Item>
<Item>
<link>https://qa.example.com/product-title/p/prod3</link>
<title>
<![CDATA[Watch Prod3 Title]]>
</title>
<description>
<![CDATA[<p>Prod4 description</p>]]>
</description>
<productType>Watch</productType>
<t1>Watches|Discount</t1>
<t2>Samsung|5%</t2>

<size>10</size>
<ProductId>prod4</ProductId>
<imageUrl>/content/dam/Images/product/prod3.jpg</imageUrl>
</Item>
</channel>
</feed>

The field “t1” defines the parent categories and filed “t2" defines the corresponding child categories. This helps to add the individual items to a different nested categories. The simple nested facet can also be defined combining existing fields with single values e.g <productType>Books</productType> and <subCategory>Legal</subCategory>

This data is converted to the following format by the filtering script during the indexing process.

e.g multiple “parent”child” categories

Book:Legal|Discount:10%

Single “parent:child” category

Books:Legal

Let us now create new metadata for fields “t1” and “t2”, Settings →Metadata →Definitions

Create a metadata definition for the nested facet e.g “n1"

Select “Allow List” if multiple “parent:child” categories need to be enabled as part of the nested facet e.g Book:Legal|Discount:10%

List Delimiters — Delimiters separate the individual “parent:child” categories.

Update IndexConnector definition with “t1” and “t2” fields, Settings →Crawling →Index Connector

Now define 3 different facets “t1” and “t2”(normal) and “n1”(nested facet), Design →Navigation →Facets

Enable “Nested Facet” option, select the parent and child facet defined earlier . Add the delimiter desperate the parent and child categories e.g “parent:child”

Let us now enable the filtering script that convert the data into the required format for nested facet, Settings →Filtering →Filtering Script

Add the below filtering script — this filtering script is enable for IndexConnector entry point with multiple value facet

# This is a complete example.
# This script is designed for index connector where each record
# in the XML file is converted into a fake HTML page filled with
# meta data tags.
my $doc;
{
# Slurp all the data
local $/;
undef $/;
$doc = <>;
}
# All legitimate index connector data has key in its URL.
# Process the page if and only if it is coming from index connector and
# it is not the first entry point page. Entry point pages don't have key
# in the URL.
if ( $main::search_url =~ /\?key=/ ) {
my $meta = {};
# Mine and scrape the meta fields from the page
my @lines = split( /\n/, $doc );
foreach my $line (@lines) {
if ( $line =~ m{<meta name="(.*?)" content="(.*?)" />} ) {
$meta->{ lc($1) } = $2;
}
}
my $t1 = $meta->{'t1'};
my $t2 = $meta->{'t2'};
if ( defined $t1 && $t1 ) {
my @t1arr = split( /\|/, $t1 );
if ( scalar @t1arr > 0 ) {
if (defined $t2 && $t2) {
my @t2arr = split( /\|/, $t2 );
if ( scalar @t2arr > 0 )
{
my $max = ((scalar @t1arr) < (scalar @t2arr)) ? (scalar @t1arr) : (scalar @t2arr);
for (my $i = 0; $i < $max; $i++)
{
$t1arr[$i] .= ":" . $t2arr[$i];
}
}
}
my $output = join( '|', @t1arr );
$doc =~ s{</head>}{<meta name="n1" content="$output" />\n</head>};
}
}
}
# Prints the entire page back out.
print $doc;

The script can be tested with sample URL

The test URL can be taken from Reports →Data Views →Default View

Refer https://docs.adobe.com/content/help/en/search- promote/using/design/c-about-facets.html for details on filtering script for URL based Indexing.

Now update the Transport template with the nested facet, Design →Templates

<search-content-type-header charset="UTF-8">
{
"general": {
"query" : "<search-query />",
"total" : "<search-total />",
"lower" : "<search-lower />",
"upper" : "<search-upper />"
},
"facets" : [
{
"name" : "n1",
"values" : [<search-field-value-list name="n1" quotes="yes" data="values" sortby="values" encoding="json" />],
"counts" : [<search-field-value-list name="n1" quotes="no" data="results" sortby="values" />]
}
]
,
"results" : [
<search-results>
{
"fields" :
[
{
"name" : "mdi",
"value" : "<search-display-field name="mdi" length="500" encoding="json" />"
},
{
"name" : "title",
"value" : "<search-display-field name="title" encoding="json" />"
},
{
"name" : "productType",
"value" : "<search-display-field name="productType" encoding="json" />"
}
]
}
<search-if-not-last>,</search-if-not-last>
</search-results>
]
}

Update the Presentation template with the nested facet

<guided-content-type-header content="application/json" />
<guided-if-query-param-defined gsname="callback" /><guided-query-param gsname="callback" />(</guided-if-query-param-defined>
{
"general" :
{
"query" : "<guided-query-param gsname='q' />",
"total" : "<guided-results-total />",
"page_lower" : "<guided-results-lower>",
"page_upper" : "<guided-results-upper>",
"page_total": "<guided-page-total/>"
},
"facets" :
[
<guided-zone gsname="Facets_Default">
<guided-facet gsname="n1" >
{
"values" :
[
<guided-facet-values>
{
"value" : "<guided-facet-value escape="ijson">",
"selected" : "<guided-if-facet-value-selected>true<guided-else-facet-value-selected>false</guided-if-facet-value-selected>",
"count" : "<guided-facet-count>",
"link" : "<guided-facet-value-path>",
"undolink" : "<guided-facet-value-undo-path>",

"child" :
[
<guided-if-facet-value-has-children>

<guided-facet-value-children>
{
"value" : "<guided-facet-value escape="ijson">",
"selected" : "<guided-if-facet-value-selected>true<guided-else-facet-value-selected>false</guided-if-facet-value-selected>",
"count" : "<guided-facet-count>",
"link" : "<guided-facet-value-path>",
"undolink" : "<guided-facet-value-undo-path>"
}<guided-if-not-last>,</guided-if-not-last>
</guided-facet-value-children>

</guided-if-facet-value-has-children>

]
}<guided-if-not-last>,</guided-if-not-last>
</guided-facet-values>
]
}
</guided-facet>
</guided-zone>
],
"results" :
[
<guided-results gsname="default">
{
"index" : "<guided-result-index />",
"title" : "<guided-result-field gsname="title" escape="ijson" />",
"productType" : "<guided-result-field gsname="productType" escape="ijson" />"
}<guided-if-not-last>,</guided-if-not-last>
</guided-results>
]
}
<guided-if-query-param-defined gsname="callback">)</guided-if-query-param-defined>

The configurations are ready, let us now run a Stage indexing (note the configuration are not pushed to live yet)

Index →Full Index →Staged Index →Run Full Index

Access http://stage-xxxxxxxxxxx.guided.ss-omtrdc.net/do=json&sp_staged=1&sp_q=*

xxxxxxxxxxx — Search and Promote account number

This will respond with JSON data which contains the required facet and meta data details.

{
"general": {
"query": "*",
"total": "4",
"page_lower": "1",
"page_upper": "4",
"page_total": "1"
},
"facets": [
{
"values": [
{
"value": "Books",
"selected": "false",
"count": "2",
"link": "?do=json;i=1;q=*;q1=Books;sp_staged=1;x1=t1",
"undolink": "",
"child": [
{
"value": "Legal",
"selected": "false",
"count": "1",
"link": "?do=json;i=1;q=*;q1=Books;sp_staged=1;x1=t1;i=1;q2=Legal;x2=t2",
"undolink": ""
},
{
"value": "Tax",
"selected": "false",
"count": "1",
"link": "?do=json;i=1;q=*;q1=Books;sp_staged=1;x1=t1;i=1;q2=Tax;x2=t2",
"undolink": ""
}
]
},
{
"value": "Discount",
"selected": "false",
"count": "4",
"link": "?do=json;i=1;q=*;q1=Discount;sp_staged=1;x1=t1",
"undolink": "",
"child": [
{
"value": "10%",
"selected": "false",
"count": "2",
"link": "?do=json;i=1;q=*;q1=Discount;sp_staged=1;x1=t1;i=1;q2=10%25;x2=t2",
"undolink": ""
},
{
"value": "5%",
"selected": "false",
"count": "2",
"link": "?do=json;i=1;q=*;q1=Discount;sp_staged=1;x1=t1;i=1;q2=5%25;x2=t2",
"undolink": ""
}
]
},
{
"value": "Watches",
"selected": "false",
"count": "2",
"link": "?do=json;i=1;q=*;q1=Watches;sp_staged=1;x1=t1",
"undolink": "",
"child": [
{
"value": "Gramini",
"selected": "false",
"count": "1",
"link": "?do=json;i=1;q=*;q1=Watches;sp_staged=1;x1=t1;i=1;q2=Gramini;x2=t2",
"undolink": ""
},
{
"value": "Samsung",
"selected": "false",
"count": "1",
"link": "?do=json;i=1;q=*;q1=Watches;sp_staged=1;x1=t1;i=1;q2=Samsung;x2=t2",
"undolink": ""
}
]
}
]
}
]
,
"results": [
{
"index": "",
"title": "Book Prod1 Title",
"productType": "Book"
},
{
"index": "",
"title": "Book Prod2 Title",
"productType": "Book"
},
{
"index": "",
"title": "Watch Prod3 Title",
"productType": "Watch"
},
{
"index": "",
"title": "Watch Prod3 Title",
"productType": "Watch"
}
]
}

The configuration can be pushed live after successful validation and run a live index →Full Index →Live Index →Run Full Index

The URL to access live data http://xxxxxxxxxxx.guided.ss-omtrdc.net/do=json&sp_q=*

The facet data in the response can be used to present the filtering options to users to narrow down the website search.