Cross-Type Recommendation Using a Shared Media Feature Space
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Solution Overview
Problem
Existing recommendation algorithms for media content, such as live streaming and short videos, often ignore user behavior signals from different types of media, leading to limitations in personalized recommendations, especially for new users and across different media types.
Innovation Solution
A method for cross-type recommendation that maps different types of media content, such as short videos and live streaming, into a shared feature space, allowing for the use of user behavior signals from one type of media to enhance recommendations for another type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing recommendation algorithms are used for single media type, then recommendation accuracy for that specific media type is maintained, but cross-media recommendation capability and personalization accuracy deteriorate
Solution Approach 1:
The patent merges different media types (short videos and live streaming) into a unified feature space by mapping their respective features to a common representation. This allows the system to combine user behavior signals from both media types and use them jointly for recommendation, thereby achieving cross-media personalization without sacrificing accuracy for individual media types.
Solution Approach 2:
The patent creates a universal feature space that can represent both short video and live streaming content using the same encoding framework. This universal representation enables the system to handle multiple media types with a single recommendation model, improving adaptability while maintaining personalization accuracy through unified user preference learning.
2Measurement precision
If user behavior signals from different media types are integrated, then personalization accuracy improves, but system complexity increases
Solution Approach 1:
The patent combines features from different media types into a unified feature space using a common encoding model. By merging the feature representation mechanisms, the system achieves cross-media personalization without requiring separate complex processing pipelines for each media type, thus controlling overall system complexity.
Solution Approach 2:
The patent introduces a feature mapping mechanism as an intermediary that translates different media type features into a common representation space. This intermediary layer simplifies the integration process by providing a standardized interface between diverse data sources and the recommendation system, reducing direct complexity in handling multiple media types.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an apparatus, an electronic device, and a computer program product for cross-type recommendation. The method includes determining a first type of media content interacted with a user, and determining a first media feature of the first type of media content. The method further includes recommending a second type of media content to the user based on the first media feature, wherein the first type of media content and the second type of media content belong to different types of media content and share a same feature space.