How to implement the recommendation list function in WeChat applet with PHP

With the popularity of mobile Internet, WeChat mini programs have become an indispensable part of people's lives. It not only provides a variety of rich functions, but also recommends content suitable for users, greatly improving the user experience. In the WeChat applet, the recommendation list is one of the very important functions. This article will introduce how PHP implements the recommendation list function in the WeChat applet.

1. Recommendation algorithm

The recommendation algorithm is an important basis for realizing the recommendation list function. It determines the content and sorting method of the recommendation list. Currently commonly used recommendation algorithms include content-based recommendation, collaborative filtering recommendation, deep learning recommendation, etc. Here we take the collaborative filtering algorithm as an example to introduce the implementation method of the recommendation list.

Recommendation systems based on collaborative filtering algorithms are mainly divided into two categories: user-based collaborative filtering recommendations and item-based collaborative filtering recommendations. The former makes recommendations by calculating the similarity between users, and the latter makes recommendations by calculating the similarity between items. When implementing the recommendation list function in the WeChat applet, we can choose two methods:

1) User-based collaborative filtering recommendation

User-based collaborative filtering recommendation mainly calculates users The similarity between them is used to recommend content that similar users like. The specific implementation method is as follows:

1.1 Construct the user-item rating matrix

First, you need to construct a user-item rating matrix. Each element in the matrix represents a user's rating for an item. If the user has not rated the item, the position is 0.

1.2 Calculate the similarity between users

Next, you need to calculate the similarity between users. Here we can use Pearson correlation coefficient or cosine similarity to calculate the similarity between users. The larger the calculated similarity value is, the more similar the two users are.

1.3 Find a group of users who are similar to the target user

To find a group of users who are similar to the target user, you can sort the similarity of all users and select the group with the highest similarity. Users make recommendations.

1.4 Find a group of products that are similar to the target user

To find a group of products that are similar to the target user, you can select A group of products with the highest interest are recommended.

Through the above steps, the user-based collaborative filtering recommendation algorithm can be implemented.

2) Item-based collaborative filtering recommendation

Item-based collaborative filtering recommendation mainly recommends similar items by calculating the similarity between items. The specific implementation method is as follows:

2.1 Construct an item-user rating matrix

First, you need to construct an item-user rating matrix. Each element in the matrix represents the score of an item rated by a user. If the user has not rated the item, the position is 0.

2.2 Calculate the similarity between items

Next, you need to calculate the similarity between items. Similarity between items can be calculated using cosine similarity or Jaccard similarity. The larger the calculated similarity value is, the more similar the two items are.

2.3 Find a group of items that are similar to the target item

To find a group of items that are similar to the target item, you can sort the similarity of all items and select the group with the highest similarity. Items are recommended.

2.4 Find a group of users who are interested in the target item

To find a group of users who are interested in the target item, you can calculate the impact of the target item on items that have not been rated by this group of users. , select a group of users with the highest influence for recommendation.

Through the above steps, the item-based collaborative filtering recommendation algorithm can be implemented.

2. Implementation of recommendation list

When implementing the recommendation list, we can solve the problem through the following steps:

2.1 Obtain user information

First, you need to obtain the user's information, such as user ID, user browsing history, etc.

2.2 Use the recommendation algorithm to calculate the recommendation list

According to the user information, use the recommendation algorithm mentioned above to calculate the list of items that the user may be interested in. The calculated recommendation list can be calculated according to the degree of interest. Sorting, items ranked higher can be recommended to users.

2.3 Display the recommendation list

Display the calculated recommendation list to the user, and the user can choose whether to browse or purchase the products.

3. Conclusion

Through the above steps, we can implement the recommendation list function in the WeChat applet. The selection and implementation of the recommendation algorithm will directly affect the accuracy and user experience of the recommendation list, so in actual development, selection and optimization need to be based on actual business needs.

The above is the detailed content of How to implement the recommendation list function in WeChat applet with PHP. For more information, please follow other related articles on the PHP Chinese website!