Recommendation Engine - Configuration
The Recommendation Engine Module offers recommendations based on product and category interrelations, as well as on field-based criteria.
Scroll down to review available settings.
Response settings (1)
Here you can configure the maximum number of product recommendations that the Recommendation Engine outputs and the maximum time it spends searching for recommendations.
The Recommendation Engine is so fast that 20 recommendations or more can be called.
Personal recommendations (2)
If you have licensed the personalization module from FACT-Finder in addition to the Recommendation Engine, you can also have recommendations personalized for your customers, provided that corresponding products are available.
As with personalization, either a session-based method is selected here (FACT-Finder personalizes on the basis of the clicks, shopping cart additions, and purchases made in the session), or personalization of recommendations on the basis of a user ID assigned by the store. With a user ID solution, products purchased since the start of personalization tracking are also included in the personalized recommendations.
First, specify whether the recommendations should be personalized.
The increase max per category by factor is the factor by which the categories are multiplied. It controls from how many recommendations in a category FACT-Finder should pick out personalized ones.
By default, this value is set to 10. Do not set it too high, because this increases the computing effort - and thus the time required - for FACT-Finder to expand the recommendations to the point where personalization makes sense. If you set the value too low, it is possible that no products corresponding to the preference will be found. In this case, personalization will not take place.
Matrix settings (3)
The individual category relations of a level are displayed in the form of a matrix. Here you can view the determined category relations and, if necessary, change them according to your ideas.
The matrix settings are particularly important because the matrices themselves, their weighting and the self-relation have a great influence on the behavior of the Recommendation Engine. The higher a matrix is weighted, the greater it's influence. For example, if a matrix that accesses the brand field is the highest weighted, FACT-Finder will give the brand relationships defined in this matrix the highest priority in recommendation determination. Only then come categories, for example.
ADD MATRIX (1): here you can add one more matrix (visible in the form of a matrix under Recommendations/Matrix). The example in the screenshot has 2 matrixes defined: one for the field "Brand" and one for the field "Category".
Field (2): in this column it is possible to set the related field of a matrix.
Weight (3): here you can set some of the preconfigured sensible values for the Weight value (4). Usually here the value "Normal" works best.
Weight value (4): these values will be set automatically when a value for (3) was chosen. An arbitrary value (use carefully!) can be chosen if Weight (3) has the value "Expert".
Max. products per value (5): here you can set an upper bound for the number of recommended products (in a recommendation result) that are recommended according to a field.
Self relation (6): if "Self relation" is enabled, for example for the field "Brand", it can happen that the recommended (according to the field "Brand") products have the same brand as the product which is the base for the recommendation. If "Self relation" is disabled only different brands are taken into account for the recommendation.
Copy matrix setting to a different channel (7): clicking this icon opens a dialog which makes it possible to copy a matrix setting to a different channel.
Delete (8): click this button to delete this matrix setting.
Import settings (4)
By re-importing the Recommendation data, the product and category relations for the recommendations are recalculated and the matrices are created under the Recommendations menu item.
