Text-CNN Audiovisual Playlist Recommendation System
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Solution Overview
Problem
Current interactive information recommendation systems fail to provide personalized audiovisual playlists to users, leading to information overload and poor interaction, as they do not actively adapt to user interests or concerns.
Innovation Solution
A method and system utilizing a text-attentional convolutional neural network (Text-CNN) to process audiovisual introduction texts, extract hidden features, and generate personalized playlists by calculating user and movie similarities, enabling a voice-controlled recommendation engine for smart audiovisual playlist pushing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional passive interactive recommendation systems are used, then information can be pushed after user subscription, but the system cannot provide personalized playlists and leads to information overload
Solution Approach 1:
The patent replaces traditional mechanical recommendation systems with a neural network-based intelligent system. The Text-CNN model automatically analyzes user feedback and generates personalized recommendations without requiring manual subscription actions, thereby reducing information overload while improving personalization capability.
Solution Approach 2:
The system enables self-service by automatically processing user feedback and generating recommendations without requiring active user subscription. The neural network continuously learns from user interactions and autonomously pushes personalized audiovisual playlists, eliminating the need for manual information filtering by users.
2Quantity of substance
If traditional recommendation systems retrieve many results, then comprehensive information is provided, but interaction quality deteriorates and user needs are not met
Solution Approach 1:
The patent extracts only the most relevant information for personalized recommendations using the Text-CNN model. Instead of retrieving and presenting all available information, the system selectively extracts and pushes only the audiovisual content that matches user preferences, thereby maintaining interaction quality while providing sufficient information quantity.
Solution Approach 2:
The system changes the parameter of information presentation from quantity-based to quality-based filtering. The neural network adjusts recommendation parameters dynamically based on user feedback, transforming the recommendation approach from providing comprehensive but overwhelming information to delivering targeted, high-quality personalized content.
3Ease of operation
If voice control is integrated for smart audiovisual playlist pushing, then user convenience is improved and traditional UI limitations are avoided, but system complexity increases
Solution Approach 1:
The patent integrates multiple functions into a unified voice-controlled system. The Text-CNN model handles both user feedback analysis and recommendation generation, while the voice interface consolidates multiple interaction channels into a single natural language interface, improving convenience without proportionally increasing system complexity through functional integration.
Data Source
AI summary
In some embodiments, methods and systems for pushing audiovisual playlists based on a text-attentional convolutional neural network include a local voice interactive terminal, a dialog system server and a playlist recommendation engine, where the dialog system server and the playlist recommendation engine are respectively connected to the local voice interactive terminal. In some embodiments, the local voice interactive terminal includes a microphone array, a host computer connected to the microphone array, and a voice synthesis chip board connected to the microphone array. In some embodiments, the playlist recommendation engine obtains rating data based on a rating predictor constructed by the neural network; the host computer parses the data into recommended playlist information; and the voice terminal synthesizes the results and pushes them to a user in the form of voice.