Recommendation

Recommendation

About this Module

The Recommendation Module allows you to set up a Business Matchmaking matrix to connect attendees, speakers, sponsors/exhibitors, and propose conference sessions based on their interests.

This module serves as an "inlet" for the matrix. Once set up, you can create a Recommendation block in the livepages to display the recommendations.

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If this page is for Matrix settings, where can I enable Recommendation for the event? If I do not set add any Rules, can I still use the Recommendation feature? → Yes, the engine will learn from your participant's behaviors within the sites and suggest relevant leads and sessions to them.

Add Rules

Clicking on the "Add Rule" button gives you a pop up on the right-hand side as shown below:

Name

Give a Name to the Rule that you are setting.

Type

To select the relevant Rule Types from the drop-down:

  • People to People

The rule is for Participants Networking.

  • People to Session

The rule is for Session Matching.

  • People to Products

The rule is for Product Matching.

  • People to Sponsor

The rule is for Sponsor company Matching (Not to be confused with sponsor people which is catered by People to People type).

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Priority

The priority that you would like to give to this rule

Rule with priority 1 will have the highest priority / importance / weight and will be lower as the priority field value increases. Rules can also have equal priorities.

Criteria

This option allows us to select any one out of the 3 different criteria for recommendations:

  • Inclusion - A normal recommendation rule
  • Exclusion - A rule to exclude a particular set of attendees, sessions, products, or sponsors from being recommended to another set of attendees.
  • ExcludeAll - A rule to completely exclude a set of attendees, sessions, products, or sponsors from recommendations
NOTE: Criteria will soon be available for the all types of rules.

Groups

Groups can be created in cases where it is required to apply a single or multiple rules to a particular group of Attendees.

Source Field

This can be any one of the fields, available to people on the registration forms.

Example:

If you select Job Title, it will use the values from the Job Title field as source to match with that of the target field.

Field Value

If you want to recommend some specific source value to specific target value then you can specify the source value in this text field.

Leave this text field empty if you want all possible source values to be used for comparison.

Comparison Type

  • Exact Match
    • Also known as Syntactic Match. This will generate recommendations for every exact match between source and target values.
  • Semantic Match
    • In general, semantic means the meaning of a word or a sentence. In this type, the text semantics in the source and target values will be compared using NLP techniques.

Target Field

The target field values are compared with the source field values to generate recommendations.

Example:

If you select Job Title, it will use the values from the Job Title field as the target to match with that of the source field.

Two Way Matching

In Rules where Source Field and Target Fields are specified only the Target will be recommended to the Source. Two-Way-Matching allows us to recommend "Source to Target" in addition to the usual "Target to Source".

NOTE: Two Way Matching is available only for the People to People type.

Example Screenshots

In this example, we define a rule named "Similar Job Title" for "People to People" type of recommendation. The Source and target field are jobTitle and their corresponding Field values are left empty and the type is Exact Match.

This will generate attendee recommendations for people who have similar job titles.

Example: Attendees having similar job titles will be recommended to each other.

This rule will generate session recommendations for people whose job title semantically matches with the session's description.

Example: An attendee with a job title Machine Learning Engineer will get recommendations of Sessions having descriptions related to Machine Learning. An example of such a session description is given below:

“In this session our keynote speaker Mr. Dustin Porier who works as an AI Engineer in UFC will be explaining about the entire machine learning life cycle with its implementation using AWS Sagemaker”

This rule generate recommendations of people working in Nokia Company for Microsoft Employees and vice-versa.

Since the field value and target value text fields are populated with the company Nokia and Microsoft respectively, attendees who work in Nokia will be recommended to attendees working in Microsoft and vice versa.

This rule will generate session recommendations for people whose Interests field values match with the session tags.

Example: An attendee with Interest in Economics will get recommendations of Sessions having a tag Economics.

Target Field Value

Similar to Field Value, this can be used in situations where you want to recommend some specific source value to a specific target value. You can specify the target value in this text field.

Leave this text field empty if you want all possible target values to be used for comparison.

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