High-speed intelligent recommendation algorithm and its implementation method in PHP

With the popularity of the Internet and e-commerce, recommendation systems have received more and more attention and attention. The essence of the recommendation system is to analyze and mine users' behavioral data to provide personalized recommendation services and improve users' experience and purchase rate. In recommendation systems, algorithms are one of the key factors. This article will introduce the high-speed intelligent recommendation algorithm and its implementation method in PHP.

1. What is a high-speed intelligent recommendation algorithm?

The fast intelligent recommendation algorithm (Fast Intelligent Recommender System, FIRS) is a new type of recommendation algorithm. Compared with traditional collaborative filtering algorithms, the FIRS algorithm has higher recommendation efficiency and better recommendation accuracy. The FIRS algorithm uses matrix decomposition technology and feature selection technology to quickly find items similar to the target user's interests in massive data and provide personalized recommendation services.

2. Implementation method of FISR algorithm

1. Data processing

Before using the FIS algorithm for recommendation, you need to prepare relevant data. The data can come from the user's historical behavior records, the attribute characteristics of the item, and the user's basic information, etc. The data needs to be processed into a sparse matrix, the behavior of users is listed as items, and the elements are the user's ratings or behaviors on the items.

2. Matrix decomposition

The core of the FIS algorithm is matrix decomposition. In matrix decomposition, the sparse matrix is ​​decomposed into two dense matrices, one matrix represents the user's interest preferences, and the other matrix represents the attribute characteristics of the item. Then, the user's rating of the item is calculated based on the attribute characteristics of the item to make recommendations. Matrix decomposition requires the use of techniques such as numerical optimization and matrix operations. Some open source mathematical libraries can be used in PHP to implement matrix decomposition.

3. Feature selection

During the matrix decomposition process, a large number of user and item features will be generated. In order to improve recommendation efficiency and accuracy, feature selection is required, that is, selecting the most useful features from all features. In PHP, you can use feature selection algorithms based on information gain or chi-square test.

4. Recommendation calculation

After matrix decomposition and feature selection, recommendation calculation can be performed. Recommendation calculation can be achieved through the following steps:

(1) Calculate the user's interest preference matrix and the attribute characteristic matrix of the item based on the user's historical behavior records and the attribute characteristics of the item.

(2) Calculate the user's rating of the item based on the user's historical behavior record and the attribute feature matrix of the item. You can use cosine similarity or a method based on a probability model.

(3) Select the item with the highest user rating as the recommendation result.

3. Advantages and Disadvantages of FIS Algorithm

The FIS algorithm has excellent performance in both recommendation efficiency and accuracy. Due to the use of matrix decomposition and feature selection technology, items similar to the target user's interests can be quickly found and personalized recommendation services can be provided. Compared with the traditional collaborative filtering algorithm, the FIS algorithm has the following advantages:

(1) The FIS algorithm has no restrictions on the scale and density of data and can handle massive user and item data.

(2) The FIS algorithm performs well in handling the cold start problem, that is, it can also provide better recommendations for new users or new items.

(3) The FIS algorithm does not require users’ historical rating data, only the attribute characteristics of users and items.

However, the FIS algorithm also has some shortcomings, such as:

(1) The FIS algorithm requires feature selection, which requires certain computing resources.

(2) The FIS algorithm is sensitive to noisy data and requires data cleaning and preprocessing.

(3) The FIS algorithm is not as effective as the collaborative filtering algorithm in recommending long-tail items.

4. Conclusion

The FIS algorithm is an efficient and accurate recommendation algorithm that is widely used in e-commerce, social networks and other fields. In PHP, you can use open source mathematics libraries and machine learning libraries to implement the FIS algorithm. In addition, it can also be combined with other recommendation algorithms, such as collaborative filtering algorithms, deep learning algorithms, etc., to improve the effect and accuracy of recommendations.

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