Laravel Love: Likes and Dislikes for Eloquent Models

Laravel Love is a package by Anton Komarev that lets people express how they feel about content by liking and disliking Eloquent models. The package works by defining a “liker” model defined with a contract. Most likely this will be your application’s User model:

<?php

use Cog\Contracts\Love\Liker\Models\Liker as LikerContract;
use Cog\Laravel\Love\Liker\Models\Traits\Liker;
use Illuminate\Foundation\Auth\User as Authenticatable;

class User extends Authenticatable implements LikerContract
{
    use Liker;
}

On the other end, you define Eloquent models that can be “liked” by your “liker” model:

use Cog\Contracts\Love\Likeable\Models\Likeable as LikeableContract;
use Cog\Laravel\Love\Likeable\Models\Traits\Likeable;
use Illuminate\Database\Eloquent\Model;

class Article extends Model implements LikeableContract
{
    use Likeable;
}

Here are some available methods this package affords your models:

$user->like($article);
$user->likedBy(); // true/false
$user->unlike($article);
$user->toggleLike($article);

// Dislike
$user->dislike($article);

On likable models you here are some of the methods available:

<?php

$article->likesCount;
$article->dislikesCount;

This package also provides model scopes that you can learn about in the GitHub readme, along with installation instructions and API documentation:

<?php

# Find all articles liked by a user
Article::whereLikedBy($user->id)
    ->with('likesCounter') // Allow eager load (optional)
    ->get();

# Get Articles sorted by likes count asc
$sortedArticles = Article::orderByLikesCount('asc')->get();

# The default sort order is desc
$sortedArticles = Article::orderByLikesCount()->get();

The obvious use-case for this package is showing the number of likes/dislikes on content and even sorting content based on that (i.e., Reddit or Hacker News upvotes).

Another use-case that intrigues me more is trying to recommend content to users, and allowing a user to provide feedback on whether they like or dislike specific recommendations. You could use that data to keep improving the recommended content based on user feedback