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[Utility & Helpers](/categories/utility) 4. / 5. aldeebhasan/laravelcf ActiveLibrary[Utility & Helpers](/categories/utility) aldeebhasan/laravelcf ===================== This package will allow you to make fast recommendation based on custom inputs 1.0.3(2y ago)3111MITPHPPHP >=8.1 Since Jun 10Pushed 2y ago1 watchersCompare [ Source](https://github.com/aldeebhasan/LaravelCF)[ Packagist](https://packagist.org/packages/aldeebhasan/laravelcf)[ RSS](/packages/aldeebhasan-laravelcf/feed)WikiDiscussions master Synced 1mo ago READMEChangelog (4)Dependencies (2)Versions (5)Used By (0) Laravel Recommendation system (LaravelCF) ========================================= [](#laravel-recommendation-system-laravelcf) A php package allow you to find the est recommendation of your modules Installation ------------ [](#installation) Install using composer: ``` composer require aldeebhasan/laravelcf ``` The run : ``` php artisan migrate ``` Basic Usage ----------- [](#basic-usage) LarvelCF package allow you to recommend data based on many algorithms including (Cosine,Weighted Cosine, Centered Cosine, SlopeOne). In general we have two kind of recommender included in this package: - item-based recommender - user-based recommender Filling the data ---------------- [](#filling-the-data) The first step to build your recommendation system is to provide the dataset you want to work with. In LarvelCF we have 4 type of data (PURCHASE, RATE, CART\_ACTION, BOOKMARK). the purpose of these types is to enable you to handle different types of data at the same time. You can use the following to enter your recommender data: ``` use \Aldeebhasan\LaravelCF\Facades\Recommender; Recommender::addRating ('user_1', 'product_1', 5); Recommender::addCartAddition('user_1', 'product_1', 2); // like the quantity Recommender::addPurchase ('user_1', 'product_1', 5); // like the quantity Recommender::addBookmark ('user_1', 'product_1', 5); ``` Instantiate the recommender --------------------------- [](#instantiate-the-recommender) After entering your data, you can instantiate your desired recommender using our support facade: ``` use \Aldeebhasan\LaravelCF\Facades\Recommender; use \Aldeebhasan\LaravelCF\Enums\RelationType; /* you cann also use any of RelationType::PURCHASE,RelationType::CART_ACTION,RelationType::BOOKMARK*/ Recommender::getItemBasedRecommender(RelationType::RATE); // to recommend similar products //OR Recommender::getUserBasedRecommender(RelationType::RATE);// to recommend similar users ``` Get recommendations ------------------- [](#get-recommendations) Finally, to make your recommendations you will run the following code: ``` use \Aldeebhasan\LaravelCF\Facades\Recommender; use \Aldeebhasan\LaravelCF\Enums\RelationType; /* you cann also use any of RelationType::PURCHASE,RelationType::CART_ACTION,RelationType::BOOKMARK*/ Recommender::getItemBasedRecommender(RelationType::RATE) ->setSimilarityFunction(Cosine::class) ->train() ->recommendTo('user_1'); ``` For the `setSimilarityFunction`, you can provide the similarity algorithm, the missing value default values, and weather you want to fill the missing methods or discard them. Available Similarity algorithm: - Cosine::class (default for item-based) - CosineCentered::class - CosineWeighted::class - Jaccard::class - SlopeOne::class - Pearson::class (default for user-based) Available missing values replacement methods: - MissingValue::ZERO (package default) - MissingValue::MEAN - MissingValue::MEDIAN ``` use \Aldeebhasan\LaravelCF\Facades\Recommender; use \Aldeebhasan\LaravelCF\Enums\RelationType; use \Aldeebhasan\LaravelCF\Enums\MissingValue; use \Aldeebhasan\LaravelCF\Similarity; Recommender::getItemBasedRecommender(RelationType::RATE) // use Weighted cosine algorithm and replace the missing values with zero ->setSimilarityFunction(CosineWeighted::class,MissingValue::ZERO,true) // use SlopeOne algorithm and replace the missing values with the mean ->setSimilarityFunction(SlopeOne::class,MissingValue::MEAN,true) ``` Full Example ------------ [](#full-example) ``` use \Aldeebhasan\LaravelCF\Facades\Recommender; use \Aldeebhasan\LaravelCF\Enums\RelationType; use \Aldeebhasan\LaravelCF\Enums\MissingValue; use \Aldeebhasan\LaravelCF\Similarity; Recommender::addRating(1, 'squid', 1); Recommender::addRating(2, 'squid', 1); Recommender::addRating(3, 'squid', 0.2); Recommender::addRating(1, 'cuttlefish', 0.5); Recommender::addRating(3, 'cuttlefish', 0.4); Recommender::addRating(4, 'cuttlefish', 0.9); Recommender::addRating(1, 'octopus', 0.2); Recommender::addRating(2, 'octopus', 0.5); Recommender::addRating(3, 'octopus', 1); Recommender::addRating(4, 'octopus', 0.4); Recommender::addRating(1, 'nautilus', 0.2); Recommender::addRating(3, 'nautilus', 0.4); Recommender::addRating(4, 'nautilus', 0.5); $results = Recommender::getItemBasedRecommender(RelationType::RATE) ->setSimilarityFunction(CosineWeighted::class, MissingValue::MEAN) ->train() ->recommendTo('squid'); /** recommendation results sorted by similarity: [ "cuttlefish" => 0.89 "nautilus" => 0.75 "octopus" => 0.5 ] **/ ``` License ------- [](#license) Laravel Recommendation system package is licensed under [The MIT License (MIT)](LICENSE). Security contact information ---------------------------- [](#security-contact-information) To report a security vulnerability, contact directly to the developer contact email [Here](mailto:aldeeb.91@gmail.com). ### Health Score 26 — LowBetter than 43% of packages Maintenance20 Infrequent updates — may be unmaintained Popularity9 Limited adoption so far Community8 Small or concentrated contributor base Maturity56 Maturing project, gaining track record Bus Factor1 Top contributor holds 100% of commits — single point of failure How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window. **Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores. **Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement. **Maturity (30%)** — Project age, version count, PHP version support, and release stability. ### Release Activity Cadence Every ~15 days Total 3 Last Release 1036d ago ### Community Maintainers ![](https://www.gravatar.com/avatar/74e7b9d93b0f666f462ad0be43dde05991e9d141f9fe840009775808107d440e?d=identicon)[aldeebhasan](/maintainers/aldeebhasan) --- Top Contributors [![aldeebhasan](https://avatars.githubusercontent.com/u/62222392?v=4)](https://github.com/aldeebhasan "aldeebhasan (1 commits)") --- Tags laravelstatisticsrecommendationcollaborative filteringproduct recommendationsfast recommendation ### Code Quality TestsPHPUnit ### Embed Badge ![Health badge](/badges/aldeebhasan-laravelcf/health.svg) ``` [![Health](https://phpackages.com/badges/aldeebhasan-laravelcf/health.svg)](https://phpackages.com/packages/aldeebhasan-laravelcf) ``` ### Alternatives [wnx/laravel-stats Get insights about your Laravel Project 1.8k1.8M7](/packages/wnx-laravel-stats)[phpjuice/slopeone PHP implementation of the Weighted Slope One rating-based collaborative filtering scheme. 858.7k](/packages/phpjuice-slopeone)[timothepearce/laravel-time-series Laravel Time Series provides an API to create and maintain projected data from you Eloquent models, and represent them as time-series. 955.4k](/packages/timothepearce-laravel-time-series)[amendozaaguiar/filament-route-statistics Filament route statictics viewer 3225.0k1](/packages/amendozaaguiar-filament-route-statistics)[cornford/googlitics An easy way to integrate Google Analytics with Laravel. 3310.2k](/packages/cornford-googlitics) PHPackages © 2026 [Directory](/)[Categories](/categories)[Trending](/trending)[Changelog](/changelog)[Analyze](/analyze)