Content Embedding Generation for Limited Interaction Data
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Solution Overview
Problem
Online systems face challenges in identifying users interested in content items with limited interaction data, particularly for content types like live video, which restricts effective content dissemination.
Innovation Solution
The system generates a content embedding based on interactions from a set of viewing users who performed specific actions with the content item, and compares this embedding with user embeddings to recommend the content to similar users, using methods like averaging or weighting embeddings based on user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the online system recommends content based on user interactions, then content distribution effectiveness is improved, but the system cannot effectively recommend content items with limited interaction data
Solution Approach 1:
The patent introduces user embeddings as an intermediary representation that mediates between limited interaction data and content recommendation. User embeddings capture user characteristics and preferences in a compressed vector form, enabling the system to recommend content items even when direct interaction data is scarce. The embedding layer transforms sparse interaction data into dense representations that can be effectively utilized for recommendation.
Solution Approach 2:
The patent transforms interaction data from its original sparse form into a different parameter space through embedding vectors. By changing the representation parameters from raw interaction counts to continuous embedding vectors, the system can effectively utilize limited interaction data and extrapolate user preferences for content recommendation.
2Quantity of substance
If the online system presents content to viewing users, then content reach is increased, but interaction data remains limited making user identification difficult
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing user embeddings before content recommendation occurs. These embeddings capture user characteristics in advance, enabling the system to immediately recommend content items to new viewing users without requiring extensive interaction data collection first. This preliminary preparation of user representations accelerates the content dissemination process.
3Measurement precision
If the online system uses traditional recommendation methods based on direct user interactions, then recommendation accuracy is improved, but content items like live video with limited interactions cannot be effectively distributed
Solution Approach 1:
User embeddings serve as an intermediary that bridges the gap between limited interaction data and accurate recommendation needs. The embedding layer transforms sparse interaction information into rich user representations that enable accurate content recommendation even for content items with minimal interaction history, thus resolving the contradiction between recommendation accuracy and dissemination efficiency.
Data Source
AI summary
An online system that recommends content to a user obtains a content item and receives limited interaction with the content item when it is obtained. The online system also maintains embeddings for various users based on interactions by the users with content presented to the users. Viewing users to whom the content item is presented and who performed one or more interactions with the content item are identified. Based on embeddings for the viewing users, the online system generates a content embedding for the content item. When the online system identifies a candidate user, the online system determines a similarity between the content embedding and an embedding of the viewing user and determines whether to recommend the content item to the candidate user based on the comparison.