How does AnQiCMS define and display related articles? Is it based on keywords or manual association?

In website operation, recommending related articles is an important strategy to enhance user experience, extend user stay time, and optimize on-site SEO.AnQiCMS provides a flexible and efficient mechanism to define and display related articles, allowing operators to choose intelligent recommendations or manual associations according to their actual needs.

Intelligent recommendation: Dynamic association based on keywords and tags

AnQiCMS's intelligent recommendation mechanism mainly relies on the keywords and tags set in the article content. When you edit articles in the background, you can set for each article:

• Document keywords:These keywords are the condensation of the core theme of the article. You can enter them manually or choose from the system-provided keyword library.Keywords are usually separated by English commas between them, not only do they help search engines understand the theme of the article, but they are also an important basis for content matching within AnQiCMS.
• Tag Label:Tags are similar to the topics or categories of articles, but they are more flexible and personalized. You can add one or more tags to an article to associate articles with common topics.

AnQiCMS will utilize this keyword and tag information, combined with internal algorithms, to analyze the relevance between articles.When a visitor browses an article, the system can intelligently recommend other articles with similar themes based on the keywords or tags of this article, thereby forming a content loop and guiding users to discover more interesting content.

This method is particularly suitable for websites with large amounts of content and frequent updates, it can greatly reduce manual operations, allowing the system to automatically discover and recommend relevant content, and improve operational efficiency.

Precise configuration: Manually associate content guidance

Although smart recommendations are very convenient, in certain specific scenarios, operators may need to control the recommendation of related articles more accurately.For example, you may want to associate a special article with a specific product page, or link a beginner's guide to an advanced tutorial.In this case, AnQiCMS provides the ability to manually associate articles.

By manually linking, you can directly specify which articles to be related reading when editing the article.This means you can skip the system's automatic judgment and directly select the article that you think can best guide users and is most in line with business logic for display.This way gives the operator complete control over content association, allowing for the fine-grained design of content recommendations based on content strategy and user conversion path.

Manually linking is particularly suitable for scenarios where it is necessary to emphasize specific content, guide users to complete specific actions (such as purchasing, registering), or build a clear knowledge system.

How to display related articles in the template?

Whether you choose smart recommendation or manual association, AnQiCMS uses powerfularchiveListtags to unify the call. This tag'stype="related"The parameter is specifically used to retrieve related articles.

Specifically, when you need to display related articles on the article detail page, you can use the following template code:

{# 获取相关文档列表 #}
<div>
{% archiveList archives with type="related" limit="10" %}
    {% for item in archives %}
    <li>
        <a href="{{item.Link}}">
            <h5>{{item.Title}}</h5>
            <div>{{item.Description}}</div>
        </a>
    </li>
    {% empty %}
    <li>
        当前没有相关文章
    </li>
    {% endfor %}
{% endarchiveList %}
</div>

Herelimit="10"Controlled the number of displayed related articles.

To distinguish between keyword-based recommendations and manual associations,archiveListthe tags also providelikeparameters:

• like="keywords"When you want the system to intelligently match keywords from the current article.AnQiCMS will find and recommend related articles based on the first keyword of the current article.
• like="relation"When you want to display the associated articles manually set in the background document editing interface.

This means you can set different values based on business needs, even on the same page,likeParameters to display a list of relevant articles with different logic, realizing a richer recommendation strategy.

This design of AnQiCMS fully considers the needs of website operation in terms of efficiency and accuracy, through intelligent keyword/tag recognition and flexible manual configuration, helping the website build a more effective content association network, thereby enhancing user experience and the overall value of the website.

Frequently Asked Questions (FAQ)

1. How are the recommended articles of AnQiCMS sorted?When usingtype="related"When retrieving the recommended articles of the smart recommendation,archiveListTags are not supportedorderThe parameters are customized for sorting. The system usually sorts based on its internal algorithm, combined with factors such as relevance and publication time, to ensure the effectiveness of recommended content.

2. Where do I need to set up the manual association of articles in the background?Based onarchiveListin the taglike="relation"The description, manual association of articles needs to be configured in the 'related documents' area of the 'background document editing interface'.This usually means that when editing the details page of an article, there will be special options or modules for you to select or enter the ID or title of other articles to establish the association between them.

3. Can I use both keyword intelligent recommendation and manual association at the same time?Of course you can. AnQiCMS's flexibility allows you to be positioned or accessed in different ways.archiveListTag instance, calling the article list based on keyword intelligent recommendation and the manually associated article list.For example, you can first display several manually associated 'Special Recommendations' at the bottom of the article, and then show more 'Articles You May Also Like' based on keywords below that.