Spelling Correction and Did You Mean (DYM) - Endeca Features

Spelling Correction operates by computing alternate spellings for user query terms, evaluating the likelihood that these alternate spellings are the best interpretation, and then using the best alternate spell-corrected query forms to return extra search results. 

Example:  A user might search for records containing the text skrts. With spelling correction enabled, the Endeca MDEX Engine will return the expected results: those containing the text skirts.

Did You Mean (DYM) functionality allows an application to provide the user with explicit alternative suggestions for a keyword search. 

Examples #1:  User searched for denms and the Endeca MDEX Engine will return the expected results those containing the text denim and also DYM suggestions for downs

Examples #2:  User searched for blk and the Endeca MDEX Engine returned no results but has DYM suggestions for back, belt & black

What is Endeca Precedence Rules? - Endeca Features

Precedence rules provide a way to delay the display of dimensions until they offer a useful refinement of the navigation state. Precedence rules are defined in terms of a trigger dimension value and a target dimension, where a user's selection of the trigger dimension value reveals the previously unavailable target dimension to the user.
Example: Assume we have two dimensions: Size Type and Size.

We have one precedence rules:   Size Type> Size

In this case, the Size dimension is displayed after a dimension value from Size Type dimension is selected.

Option	Description
Source dimension value	The dimension that serves as a trigger for the To (or target) dimension value to be displayed.
Target dimension	The dimension that serves as a target for the From (or source) dimension value.
Rule type	There are two types of source dimension values: The standard type means that if the dimension value specified as the trigger or any of its descendants are in the navigation state, then the target is presented (one trigger, one target). The leaf type means that querying any leaf dimension value from the trigger dimension will cause the target dimension value to be displayed (many triggers, one target).

 

Stemming and Thesaurus - Endeca Features

Stemming  - The stemming dictionary is based on the common English dictionary, and doesn’t pluralize proper nouns, brand names, etc.  In order to ‘Stem’ a plural of a word that doesn’t occur commonly, a two way thesaurus entry should be made in the Workbench or update the stemming dictionary.

      Stemming  dictionary found at MDEX level /opt/app/endeca//MDEX/6.5.1/conf/stemming

    Thesaurus  -  The thesaurus is intended for specifying concept-level mappings between words and phrases.

There are two options available when configuring thesaurus entries:

1)      One-Way - This mapping technique specifies only one direction of equivalence. 

                 Ex: Assume you define a one-way mapping from the phrase “red wine” to the phrases “merlot” and “cabernet sauvignon”.  This one-way mapping ensures that a search for “red wine” also returns any matches containing the more specific terms “merlot” or “cabernet sauvignon.”

2)      Two-Way - This technique means that the direction of a word mapping is equivalent between the words.

                  Ex: a Two-way mapping between “laptop,” “desktop,” and “notebook” means that a search for one of these words will always return all results matching any of these words

Note: Only one global thesaurus is supported for an Endeca data domain. In other words, language-specific thesauruses are not supported (for example, one thesaurus for English, a second for French, and so on).  

Endeca Relevance Ranking - Best practice

Best Practice: For applications dealing with Retail catalog data, the preferred order of modules is:
1. NTerms
2. MaxField
3. Glom
4. Exact
5. Static

Explanation: 
NTerms, the first module, ensures that in a multi-word search, the more words that match in the record, the higher the record is scored.
MaxField puts cross-field matches (see Allowing Cross-Field Matches) as high in priority as possible, to the point where they could tie with non-cross-field matches.
Glom, decomposes cross-field matches, effectively breaking any ties resulting from MaxField. Together, MaxField and Glom provide appropriate cross-field match ordering, depending upon what matched.
Exact module means that an exact match in a highly-ranked member of the search interface is placed higher than a partial or cross-field match.
Optionally, the Static module can be used to sort remaining ties by criteria such as Price or InstockWeb.

Example: Below Screenshot shows on how to configure Rel.Ranking Modules using Endeca Pipeline



Best Practice: For applications dealing with Document Repository Data, the preferred order of modules is:
1. NTerms
2. MaxField
3. Glom
4. Phrase
5. Static

Explanation:
NTerms, the first module, ensures that in a multi-word search, the more words that match in the record, the higher the record is scored.
MaxField puts cross-field matches (see Allowing Cross-Field Matches) as high in priority as possible, to the point where they could tie with non-cross-field matches.
Glom, decomposes cross-field matches, effectively breaking any ties resulting from MaxField. Together, MaxField and Glom provide appropriate cross-field match ordering, depending upon what matched.
Phrase module ensures that results containing the user's query as an exact phrase are given a higher priority than matches containing the user's search terms sprinkled throughout the text.
Optionally, the Static module can be used to sort remaining ties by criteria such as Price or SalesRank.