Personalization and Recommendations

The User Recommendation and Personalization Engine are designed to predict and show the videos that a user would like to watch. According to various user surveys on OTT, around 44% of users end up closing or uninstalling the app when they are confused about what to watch. 

Top reasons why OTT apps use Video Recommendation Engine

- User Recommendations are the #1 driver of consumer engagement in the App and help in retaining users.
- Consumption of content also be determined by the extent to which an app or platform can personalize the experience and keep users engaged.
- Video recommendation helps in increasing revenue and customer satisfaction.

Viewlift Recommendation Architecture runs on Amazon Personalize 

How the Recommendation Engine works in the background

We consider three datasets for Model Training: 

1. User Item Interactions Dataset
   This contains all the History (What videos user has watched in the past) USER_ID, ITEM ID, EVENT _TYPE, EVENT _VALUE, TIMESTAMP, WATCHED_DURATION
   
   
2. Users Data Set
   This contains the User Preferences (Demographic info and Genres the user likes) USER ID, GENRE
   
   
3. Video Item Data Set
   This contains the Video Details with the Kind of Genres/Categories they are linked to ITEM_ID, GENRE

Regular Updates

• Data Refresh: These datasets are regularly updated in Amazon Storage Bucket to capture the most recent user interactions and videos.
• Retraining: At a frequency of five times a month, the recommendation model is retrained on the updated datasets to incorporate new user preferences and video content.

Data Ingestion Flow through Amazon Personalize

 

Enable Recommendation & Personalization: AppCMS Settings

From the ViewLift Tools Application, navigate to the AppCMS module > Settings > scroll to the Recommendation Tab.

• Enable Recommendation – Enabling this toggle will start training the Model for the user based on past behavior
  of videos what users have seen and existing videos in the system. A user's watch history will modify the Recommendation results.
  Note: If a user's recommendations aren't relevant to their interests, they can clear their search and watch histories. Make sure to tag your videos with the appropriate category to improve user recommendations.
• Enable Personalization –  For new users in the system, it will ask for the genres that users love while signing up. The Personalization Categories entered in the Recommendation section will be visible to users when they sign up/edit Personalization. Learn more: Create Personalization Trays.
• Enable Recommendation AutoPlay– Will play the recommended Videos in AutoPlay on Player Screen
   

Recommendation Tray Added  and Recommendation Autoplay 

Add/Edit Recommendation Tray on a Page

Note

The Recommended Popular Tray Module is no longer in use. The Recommendation logic is enabled in the Recommendation Tray Module and includes personalized and popular content recommendations.

Edit Recommendation Tray required screen details

Once you're happy with the changes, click Publish to publish the page live.