How to achieve accurate push of community content in Discuz environment?

In the Discuz community, accurate push of content is an important way to improve user experience and activate user participation. This article will introduce how to achieve accurate push of community content in the Discuz environment and provide specific code examples.

1. User preference data collection

To achieve accurate push, we first need to collect user preference data and understand the user’s interests, hobbies, areas of concern and other information. Data can be collected in the following ways:

1. User registration information collection: When users register, set relevant fields to allow users to fill in their personal interests and hobbies, such as favorite topics, Sections of concern, etc.
2. User Behavior Data Analysis: Analyze users’ browsing, likes, comments and other behavioral data in the community to understand the types of content that users pay attention to.
3. Questionnaire: Conduct regular surveys to ask users about their preferences for community content, thereby collecting user preference data.

2. Content tagging

Tagging community content will help match the content with user preference data and achieve accurate push. Adding tags to community content, such as topic tags, keyword tags, etc., can be done through the following sample code:

<span class="tag">科技</span>
<span class="tag">数码</span>
<span class="tag">生活</span>

3. Implementation of recommendation algorithm

Utilize user preference data and content tagged information , which can achieve accurate push of content through recommendation algorithms. Commonly used recommendation algorithms include collaborative filtering recommendations, content recommendations, etc. The following is a simple sample code for collaborative filtering recommendation:

# 用户偏好矩阵
user_preference = {
    'user1': {
        'tag1': 1,
        'tag2': 0,
        'tag3': 1
    },
    'user2': {
        'tag1': 0,
        'tag2': 1,
        'tag3': 1
    }
}

# 计算用户之间的相似度
def calculate_similarity(user1, user2):
    common_tags = [tag for tag in user1 if tag in user2]
    if len(common_tags) == 0:
        return 0
    numerator = sum(user1[tag] * user2[tag] for tag in common_tags)
    denominator = (sum(user1[tag] ** 2 for tag in user1) * sum(user2[tag] ** 2 for tag in user2)) ** 0.5
    return numerator / denominator

# 获取相似用户的推荐内容
def get_recommendations(user_preference, user_id):
    recommendations = {}
    for user in user_preference:
        if user != user_id:
            similarity = calculate_similarity(user_preference[user_id], user_preference[user])
            for tag, score in user_preference[user].items():
                if tag not in user_preference[user_id] or user_preference[user_id][tag] == 0:
                    recommendations[tag] = recommendations.get(tag, 0) + score * similarity
    return recommendations

# 示例调用
user_id = 'user1'
recommendations = get_recommendations(user_preference, user_id)
print(recommendations)

4. Personalized push module

In the Discuz community, the personalized push module can be implemented through plug-ins or custom development. Display recommended content on the user's homepage or personal homepage. The following is a simple PHP plug-in sample code:

// 根据用户ID获取推荐内容
function get_recommendations_by_user($uid) {
    // 调用推荐算法获取推荐内容
    $recommendations = get_recommendations($user_preference, $uid);
    // 将推荐内容保存到数据库或缓存
    // 返回推荐内容数组
    return $recommendations;
}

// 将推荐内容展示在页面上
function display_recommendations($uid) {
    $recommendations = get_recommendations_by_user($uid);
    foreach($recommendations as $tag => $score) {
        echo '<a href="/tag/'.$tag.'">'.$tag.'</a>';
    }
}

// 示例调用
$uid = 123;
display_recommendations($uid);

Conclusion

Through the above methods and code examples, community content can be accurately pushed in the Discuz environment, improving user experience and promoting user participation. . I hope this article is helpful to you, and you are welcome to continue to pay attention to the latest developments and technologies in community content push.

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