First things first, let's create some collections in Adalo for your items and users. Think of it like setting up a library. For example:

• Items: This is where you'll keep all the details about the items you want to recommend, like Name, Category, Rating, User Ratings, Description, and so on.
• Users: This collection will store user info such as Name, Email, Preferences, Rated Items (Relationships), etc.

Next, let's add some input fields or multiselect dropdowns in the user profile form to capture what users like. Make sure this data gets stored in the User collection. For instance:

• Genre Preferences
• Favorite Categories
• Previously Rated Items

Now, let's enable users to rate items. You can do this by adding a Rating field (number) for each item and linking it to the users who rated it. Add a form or custom list for rating items, and store this data in both Items and User collections.

Here's where the magic happens! Use Adalo's custom actions or visibility conditions to set up your recommendation logic. Some helpful steps include:

• Filter items based on user preferences and categories.
• Sort items by user ratings.
• Exclude items already rated or viewed by the user.

To do this, create a custom list for items and apply filters and sort conditions to base recommendations:

• Filter: Items->Category contains Logged In User->Favorite Categories
• Condition: Items->User Ratings does not include Logged In User

Let's make it look good! Create a dedicated screen to display the personalized recommendations. Use custom lists to show the filtered and sorted items. Make sure the display includes essential details like item name, category, and an action button (e.g., View Details or Rate).

Trigger the display of recommendations after specific user actions or page visits:

• On user login, direct to the recommendations screen.
• After user rates an item, refresh the recommendations screen.
• Utilize actions to navigate to the recommendation page when user preferences are updated.

Keep testing and gathering user feedback to make your recommendations even better. Adjust filters, inclusion criteria, and sorting methods based on user interaction and engagement to enhance the system over time.