Video Recommendation Selection Using Diversity and Relevance Optimization
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Solution Overview
Problem
Video delivery services face challenges in generating real-time video recommendations that are both relevant and diverse, as existing methods require significant computing resources and time, making it difficult to provide recommendations quickly enough in a real-time online environment without causing delays.
Innovation Solution
A recommendation system that uses an optimization process to select a subset of recommendations by maximizing relevance and diversity without calculating determinants, instead recursively updating numbers representing relevance and similarity, allowing for faster generation and display of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation methods are used to ensure relevance and diversity, then recommendation quality is improved, but computing resource usage and time increase significantly
Solution Approach 1:
The recommendation generation process is segmented into two distinct phases: an offline phase that performs computationally intensive determinant calculations to build a matrix of similarity scores between videos, and an online phase that uses this pre-computed matrix to quickly select diverse and relevant recommendations in real-time. This segmentation allows the system to maintain high recommendation quality while meeting real-time performance requirements.
Solution Approach 2:
The system performs preliminary computations offline to pre-calculate and store the similarity matrix between videos. This preliminary action includes computing determinant values that represent the diversity of potential recommendation subsets. By doing this work in advance, the system avoids performing these heavy calculations during real-time recommendation generation, thus resolving the contradiction between quality and speed.
2Loss of time
If real-time recommendation generation is implemented, then response time is reduced, but recommendation quality and diversity may deteriorate
Solution Approach 1:
The system segments the recommendation task into offline matrix construction and online subset selection. The offline phase builds a comprehensive similarity matrix and pre-computes determinant values for diversity evaluation. The online phase simply queries this pre-computed data to select recommendations, ensuring both real-time performance and maintained diversity quality through the use of pre-calculated metrics.
Solution Approach 2:
All computationally intensive preliminary actions including determinant calculations and similarity matrix construction are performed offline before the real-time recommendation request arrives. This allows the online phase to quickly select diverse recommendations by referencing pre-computed values, thus maintaining recommendation quality without compromising real-time response requirements.
3Measurement precision
If determinant calculations are performed to maximize diversity, then recommendation diversity is improved, but computing complexity and resource usage increase
Solution Approach 1:
The computation of determinant values for diversity measurement is segmented into the offline phase, where the full complexity of determinant calculations is performed once to build the similarity matrix. During the online phase, the system only performs simple lookups and comparisons using pre-computed determinant values, dramatically reducing computing complexity while maintaining the ability to measure and maximize diversity effectively.
Solution Approach 2:
The system performs the complex determinant calculations as a preliminary action during offline processing. By pre-computing these values and storing them in the similarity matrix, the system eliminates the need to perform complex determinant calculations during real-time recommendation generation, thus reducing computing complexity while preserving diversity measurement accuracy.
Data Source
AI summary
A method receives a candidate set of recommendations for video entities on a video delivery service in response to receiving a request to generate a page of an interface. A number for each recommendation is generated that represents a relevance rating of the respective recommendation minus a similarity rating between the respective recommendation and recommendations from the candidate set of recommendations that are added to a subset of recommendations. A recommendation is added to the subset of recommendations that has a maximum probability of being relevant to the user and diverse from the recommendations in the subset of recommendations based on the number. The method then updates the number for recommendations in the candidate set of recommendations based on adding the recommendation to the subset of recommendations. This process is iteratively performed and the subset of recommendations in the page of the interface is provided to a client device.