Cross-Format Content Recommendations for Short-Form Cold Start
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Solution Overview
Problem
Recommendation systems face challenges in accurately generating recommendations for short form media content due to limited user interactions and lower quality interaction data, leading to a 'cold start' problem when new content is added, and the difficulty in deducing user preferences from short duration content.
Innovation Solution
Utilizing long form content data to train a machine learning model that generates recommendations for short form content by transforming and configuring inputs to reduce memory requirements and improve data quality, leveraging historical interactions and metadata to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems use short form content interaction data, then they can provide recommendations for short form content, but the data quality is lower and user preferences are harder to deduce
Solution Approach 1:
The system pre-processes and transforms long form content interaction data into a format suitable for short form content recommendations before the cold start problem occurs. By preparing the recommendation model in advance with transformed data from long form interactions, the system can immediately provide accurate recommendations when short form content is added, without waiting for accumulated interaction data.
Solution Approach 2:
The system uses long form content interaction data as an intermediary to solve the data quality problem for short form content recommendations. By transforming and adapting long form interaction patterns to represent short form content preferences, the system bridges the gap between available high-quality data and the need for recommendations in a different content format.
2Measurement precision
If recommendation systems wait for user interactions with new content, then data quality improves, but the cold start problem occurs when new content is added
Solution Approach 1:
The system performs preliminary transformation of long form content interaction data into short form content recommendation capabilities in advance. This pre-processing ensures that when new short form content is added, recommendations can be generated immediately without waiting for user interactions to accumulate, thus solving the cold start problem while maintaining data quality.
3Measurement precision
If recommendation systems store and process interaction data for all content forms, then recommendation accuracy improves, but memory requirements and infrastructure costs increase
Solution Approach 1:
The system extracts only the essential interaction patterns and features from long form content data that are relevant for short form content recommendations. By selecting and transforming only the necessary data elements rather than storing and processing all interaction data, the system reduces memory requirements while maintaining recommendation accuracy.
Solution Approach 2:
The system transforms interaction data parameters from long form content context to short form content context through data transformation and configuration. By changing the representation parameters of the interaction data to match the target content form, the system achieves accurate recommendations without needing to store separate high-volume interaction datasets for each content form.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a recommendation for a media content of a first form of content based on user interactions with a second form of content. The first form of content is of a different length than the second form of content. An example embodiment operates by determining interaction based data associated with a second form of content based on a user interaction with a first media content. The interaction based data are provided to a machine learning model along with historical data indicative of a user behavior with media contents of the first form or the second form of contents, and metadata associated with the first media content. The machine learning model outputs a second media content of the first form.