--- title: "Personalization and Recommendations" slug: "personalization-and-recommendations" updated: 2023-12-05T20:46:05Z published: 2023-12-05T20:46:05Z canonical: "docs.viewlift.com/personalization-and-recommendations" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.viewlift.com/llms.txt > Use this file to discover all available pages before exploring further. # 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. ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701773545973.png)Viewlift Recommendation Architecture runs on Amazon Personalize  ## How the Recommendation Engine works in the background We consider three datasets for Model Training: ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701808340819.png) 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. ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701773744573.png)Data Ingestion Flow through Amazon Personalize ## Enable Recommendation & Personalization: AppCMS Settings From the [ViewLift Tools Application](https://tools.viewlift.com/), navigate to the **AppCMS** module > **Settings** > scroll to the **Recommendation** Tab. ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701785916739.png) - **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](/v1/docs/user-personalizationrecommendation-set-up). - **Enable Recommendation AutoPlay**– Will play the recommended Videos in AutoPlay on Player Screen ## Recommendation Tray Added and Recommendation Autoplay ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701801895173.png) ## Add/Edit Recommendation Tray on a Page After enabling the **Recommendation** in the AppCMS Settings, navigate to the Template Builder and open the modular page where you want to apply the Recommendation logic, the Home page, for instance. Drag and drop the **Recommendation Tray** to the Page. ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1701788376926.png) NoteThe 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. ![](https://cdn.document360.io/15e0c142-c823-4c77-a8e8-9d32ca937982/Images/Documentation/image-1639696425869.png) ### ### **Edit Recommendation Tray required screen details** | **Screen element** | **Description** | | --- | --- | | **Title** | Title of the Recommendations tray. | | **Tray Type** | **Personal** - show recommendations based on personalized content (User Watch History). **Popular** - show recommendations based on content popularity. The AWS PERSONALIZE system will fetch the watch data from Redshift, and predict and sort each video to show popular recommendations for unsigned-in users. | | **Content Type** | Choose the content type- **Series**, **Video**, or **Any** that you want to display in the Recommendation Tray. | | **Fetch Strategy** | Choose a fetched strategy. **Continue Watching**: recommendations using the "Continue Watching" data. **Watched History**: recommendations based on the user Watch History info. Note: Although it won't impact recommendation results, Fetch Strategy is currently **not** used for the Recommendation API. You can configure **Tray Type** as the content fetch strategy. | Once you're happy with the changes, click **Publish** to publish the page live.