Introduction
In the E-commerce world, product recommendations play a significant role in revenue generation and customer retention. Recommender Systems are the solution to this requirement. They analyze the customer’s behavior and recommend personalized products based on customers’ purchase history, browsing history, search queries, and other preferences.
Recommendation Systems are the backbone of any E-commerce website, providing customized suggestions to their users to buy products. It’s not just about increasing sales and revenue but also about providing an excellent user experience. Recommender Systems collect data from different sources like browsing history, purchase history, customer preferences, and demographics to create the recommendations for every user.
In this post, I am going to cover how you can Improve Product Recommendations with a Recommender System. I will also show a code sample using PHP with embedded HTML tags.
Improving Product Recommendations using Recommender Systems
Recommender Systems have two types, Collaborative Filtering and Content-Based Filtering. Collaborative Filtering recommends the products based on the user’s history and behavior, whereas Content-Based Filtering recommends the products based on similarity among products, like recommendations based on attributes such as color, size, price, etc.
Collaborative Filtering works well with websites which have a significant number of users, whereas Content-Based Filtering works well with the websites which have less data per user. Combining both Collaborative and Content-Based Filtering approaches, we can create an effective Recommender System.
Step 1: Collecting Data
To build a Recommender System, we must have some data. Two sets of data can be used for it – the user data and the product data. User data includes user information such as browsing history, search queries, purchase history and demographics. Product data includes product information such as the product’s characteristics, product categories, customer ratings, and reviews.
Step 2: Data Cleaning and Processing
After collecting the data, it should be cleaned and processed. The reason is that data may contain noise and outliers that can significantly affect the recommendation accuracy.
Step 3: Creating the Recommendation System
After cleaning, the data should be fed to the recommendation algorithm, which creates recommendations based on the input. Similarly, Content-Based Filtering algorithms can be used to recommend similar products based on the product’s attributes.
Step 4: Personalizing the Recommendations
As mentioned above, Recommender Systems recommend products based on the user’s behavior. The same product may be recommended to different users differently, based on the user’s preferences and behavior. Personalizing the recommendations improves customer satisfaction and customer retention.
Code Sample using PHP Client, embedded in HTML Tags
Here is an example code for a Recommender System using PHP Client with embedded HTML tags.
$client = new Client();
$client->addTransport(new GuzzleTransport(new Client()));
$res = $client->expressions([
new RangeExpression('price', ['from' => 100, 'to' => 200]),
])->search('products');
foreach ($res['hits'] as $hit) {
echo "{$hit['name']}
";
}
How WPSOLR can help?
WPSOLR is the advanced search plugin for your WordPress website, which allows you to add advanced search functionalities such as Search Across Multiple sites, Custom Indexing, Search As You Type, and WooCommerce Search. WPSOLR also provides an integrated API that allows developers to integrate search functionalities with external systems like a Recommender System.
By integrating WPSOLR with your Recommender System, you can personalize product recommendations based on your customers’ search queries and browsing history, enhancing the user experience and increasing brand loyalty.
Conclusion
Compared to traditional marketing methods, Recommender Systems provide customized recommendations to customers, which increases customer satisfaction and loyalty. As a result, it’s highly recommended to integrate your WordPress website with a Recommender System. Finally, by integrating WPSOLR with your recommendation system, you can make your E-commerce website more user-friendly and successful.
