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 本博文继续研究如何利用大语言模型（LLM）来构建播客推荐系统。

使用mongoDB 数据看存储基本数据

1. 播客表（Podcast）
2. 节目表（Episodes)
3. 用户表（User）

用户表(User)

DeepSeek 笔记：推荐的spotify listener 记录格式

1. Basic Account Information

• User ID: A unique identifier for the user (e.g., a Spotify-generated UUID).
• Username: The display name chosen by the user.
• Email Address: The email associated with the account.
• Password: Encrypted for security.
• Account Type: Free (ad-supported) or Premium (paid subscription).
• Country/Region: The user's location, which may affect available content.
• Date of Birth: Used for age verification and personalized content.
• Account Creation Date: When the user signed up for Spotify.

2. Subscription and Payment Details

• Subscription Status: Active, canceled, or trial.
• Payment Method: Credit card, PayPal, etc.
• Billing History: Records of past payments.
• Renewal Date: For Premium users.

3. Usage and Activity Data

• Listening History: Tracks, albums, and playlists the user has played.
• Recently Played: A list of recently listened-to songs.
• Playlists Created: User-generated playlists.
• Followed Artists/Users: Artists or other users the user follows.
• Liked Songs: Songs saved to the "Liked Songs" library.
• Podcasts Subscribed To: Podcasts the user follows.
• Device Information: Devices used to access Spotify (e.g., mobile, desktop, smart speaker).

4. Preferences and Settings

• Language Preference: The user's chosen language for the app.
• Privacy Settings: Whether the user's activity is public or private.
• Audio Quality Settings: Streaming quality (e.g., low, normal, high, very high).
• Explicit Content Filter: Whether explicit content is allowed.
• Social Sharing Settings: Whether the user allows sharing activity on social media.

5. Analytics and Recommendations

• Personalized Recommendations: Generated based on listening habits (e.g., Discover Weekly, Daily Mixes).
• Top Tracks/Artists: Lists of the user's most-played songs and artists.
• Listening Trends: Data on when and how often the user listens to music.

6. Security and Privacy

• wo-Factor Authentication (2FA): Whether enabled.
• Login History: Records of recent logins and devices used.
• Data Sharing Preferences: Whether the user allows Spotify to share data with third parties.

从上面的信息中，截取一部分重要的部分，构建一个用户记录。

用户

UserSchema={UserID:String,Username:String,Email_Address:String,Password:String,CountryRegion:String,Date_of_Birth:String,Language:String,Account_Creation_Date:String,
}

收听历史(UserListenHistory)

UserListenHistorySchema={episodes_id:String,listen_time:String,//收听时间completion_rate:Numeric, //收听完成率（百分比）}

关注的播客(UserFllowingPodcast）

UserFllowingPodcastSchema={
Podcast_ID:String,
Fllowing_time:String,//关注的时间}

History ，Follow，Like 的列表可以数组的方式存储在听众表中 

UserSchema={UserID:String,Username:String,Email_Address:String,Password:String,CountryRegion:String,Date_of_Birth:String,Language:String,Account_Creation_Date:String,History：HistorySchema，Follows：followsSchema，Likes：likesSchema
}

播客表（Podcast）

podcastSchema={podcast:String，uuid:String，title:String,image:String,description:Stringlanguage:Stringcategories:String,website:String,itunes_id:String，follows:Numer               }

节目表（Epicodes）

   epicodeSchema={audio:String,audio_length:String,description:String,pub_date:String,uuid:String,podcast_uuid:String,likes:number
}

推荐算法

   使用Embedding 模型，矢量数据库实现相似性检索。

基本原则

1.  用户通过相似性搜索，通过关键词，自然语言的提示，搜索心仪的播客节目，用户收听之后，这些节目将会加入收听历史列表中
2. 计算用户的特征与电影的特征的相似度，列出前10个最相似的播客节目。
3. 列出用户最近看过的前十部电影，计算出与这10部电影相似的播客节目，比如选择5部播客节目，一共列出10*5=50 部新的播客节目。
4. 计算与用户的特征相似的其它用户，列出前10个相似的用户，找出相似用户看过的播客节目（每个用户选择2部），于是推荐20部播客节目。

 其中（2）（4） 在听众少的时候，比较难采纳，可以先从（1）（3） 开始做。

实现

收集播客和节目数据集

数据集来源：

Building a Podcast Recommendation Engine | Kaggle

将数据集存入mongoDB 数据库

代码

生成矢量数据库

根据播客和节目的描述生成矢量数据库。以便进一步进行相似性搜索（Similarity Search）。

生成用户数据集

通过程序生成一个·user 数据表。收听历史通过节目相似性查询生成。

代码

结果