Use Composer to solve the dilemma of recommendation systems: andres-montanez/recommendations-bundle

While searching for a solution, I found the Symfony2 plugin andres-montanez/recommendations-bundle, which brought significant improvements to my project. This plugin is based on MongoDB and implements an item-based recommendation engine that uses Pearson distance to match similar items. Its core functions include:

• registerItem : You can specify the item type (for example: movie), label (for example: plot, action), and namespace.
• Record user interaction ( addAction ) : You can record the behavior of users, for example, user "Jon" scored 5 points for the movie "Batman".
• getRecommendations : Provide recommendations for specific users, and the results can be filtered by type, tags, and namespaces.

Using Composer to install this plugin is very simple:

 <code>composer require andres-montanez/recommendations-bundle</code>

After installation, you need to enable this plugin in the Symfony project and configure the MongoDB database. Next, define a timed job to update the similarity of items regularly. The frequency of this timing task can be set according to your data volume and update requirements, and it is usually a good starting point once a week.

In order to further optimize performance, I also built a service wrapper to wrap this recommended service. This allows me to customize according to specific needs and add multi-level cache. For example, for large data sets, user recommendation results change slowly, so users' recommendation results can be cached for 24-48 hours, which can significantly improve system response speed.

In practical applications, this recommendation system performs very well. The dataset test results used show that for the dataset with 100,000 ratings, 943 users and 1,682 items, the similarity generation takes only about 4 minutes, and the user recommendation is obtained in less than 2 seconds. For larger data sets (1 million ratings, 6040 users and 3883 items), the similarity generation time is about 90 minutes, but obtaining user recommendations is still completed in 2 seconds. These datasets are available at https://www.php.cn/link/117007d714adf33db6d2653d903ebf2d .

Overall, andres-montanez/recommendations-bundle installed and integrated into the project via Composer not only solved my recommendation system dilemma, but also brought significant performance improvements and user satisfaction improvements. The flexibility and efficiency of this plugin makes it ideal for building personalized recommendation systems.

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