Douyin is a popular short video app with hundreds of millions of users, including a large number of fans. In order to allow more people to discover and watch your videos, Douyin SEO is an essential part, especially for users with corporate or personal brands. In this process, the open source deployment of Douyin SEO source code is particularly important.
First of all, we need to understand the SEO mechanism of Douyin. Douyin's recommendation algorithm mainly has two parts, one is the content algorithm and the other is the user algorithm. The content algorithm mainly makes recommendations based on video quality, title, description and other elements; while the user algorithm makes recommendations based on factors such as viewing behavior and interactive behavior. Therefore, only when your video quality is good, attractive, and liked by users, can it be seen by more people.
Next, we need to open source deployment of Douyin SEO source code. First, we need to download and install Douyin's SDK, and then develop according to Douyin's development documents.
During the development process, we need to pay attention to the following points:
1. The title should be accurate: the title of the video should be able to accurately describe the theme and content of the video, preferably including keywords.
2. The description should be detailed: the information included in the description of the video should be as detailed as possible, and can include relevant information such as the production background and story background of the video.
3. The cover should be beautiful: a good cover can attract more people to click to watch the video.
4. The content should have highlights: There should be some attractive highlights in the video, such as interesting clips, unique themes, etc.
The technical algorithm used:
-
Recommendation algorithm: Provide users with personalized short video recommendations based on information such as user historical behavior and interest preferences, using algorithms such as collaborative filtering and content recommendation.
-
Classification algorithm: According to the content, tags and other information of the short video, text classification, image classification and other algorithms are used to add classification tags to the short video, which is convenient for users to find and browse.
-
Similarity algorithm: According to the short video's visual features, audio features and other information, use the similarity algorithm to recommend other short videos similar to the short video that the user is currently watching.
-
Clustering algorithm: Based on information such as user behavior, short video release time, and region, the clustering algorithm is used to divide short videos into different categories to provide users with more refined content classification.
-
Smart editing algorithm: According to the short videos uploaded by users, multiple short videos of users are automatically spliced into a complete short video through intelligent editing algorithms to improve the viewing experience of short videos.
-
Scoring algorithm: Based on user behavior, comments and other information, use the scoring algorithm to score short videos, and recommend high-scoring short videos to other users.
Functional structure:
1. Video management: For different platforms, different accounts, different formats, short videos with different content management, material grouping, etc., to achieve multi-resource data collection and unified management
2. Data mining analysis: Through data mining and data analysis, provide data basis, deeply understand user needs, and realize intelligent recommendation
3. Batch delivery of videos: multi-platform and multi-account, intelligent delivery of enterprise video content
4. Enterprise intelligent management: intelligent customer service online, intelligent clue mining, intelligent clue push
5. Small program management: Douyin clue collection small program, multi-channel operation, lead conversion
6. Short video SEO: intelligent algorithm recommendation, precise keyword layout, business effect export, etc.
7. Intelligent copywriting assistant: ChatGpt access, intelligent copywriting, scripts, topics, titles, etc. one-click generation
key technology
1. Data collection: Use Python's requests library for data crawling, use Selenium to simulate browser operations, and solve the Douyin anti-crawler mechanism.
2. Data processing: Use Python's regular expressions, BeautifulSoup and other libraries for data processing.
3. Keyword matching: Use Python's jieba word segmentation library for word segmentation, use TF-IDF algorithm for keyword weight calculation, and use cosine similarity algorithm for keyword matching.
4. Ranking display: use Python's Flask framework for web development, and use Echarts for data visualization.
code example
if ($scene < 3 || $scene > 60) {
$this->displayJsonError('场景数，需介于3-60');
}
$video_model= new App_Model_Douyin_MysqlVideoStorage();
$video_item = $video_model->getRowByIdSid($dv_id, $this->sid);
if (empty($video_item)) {
$this->displayJsonError('参数错误');
}
$updata = [
'dv_video_use' => $scene,
];
$ret = $video_model->updateById($updata, $dv_id);
//更改场景组合数，则需要重新生成组合
if($video_item['dv_video_use'] != $scene){
//清空素材排列组合及缓存视频
$VideoMaterialPailie_model = new App_Model_Douyin_MysqlVideoMaterialPailieStorage();Summarize
In addition, the developed content algorithm of the short video matrix system will also include some recommendation strategies, such as based on popular videos, based on current hot spots and based on user social circles, etc., to present the most attractive video content and attract users to stay on the platform.
In order to ensure the effectiveness of the algorithm, the system will also regularly collect user feedback and system data, and make optimizations and adjustments to ensure that the recommended and displayed video content is consistent with user preferences and behaviors.