Title Group Recommendation Algorithm for Relevance and Diversity
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Solution Overview
Problem
Online services face challenges in recommending digital media content to users effectively, as existing recommendation algorithms often fail to provide a diverse and relevant set of titles that cater to individual user preferences and consumption habits.
Innovation Solution
The implementation of a computer-implemented technique that uses a greedy algorithm or k-step look-ahead algorithm to select and order semantically meaningful groups of titles based on relevance, diversity, and coverage scores, ensuring that recommended titles are both relevant to the user and provide broad coverage of their interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommendation algorithm is implemented to predict digital media content based on user history, then the relevance of recommended titles to user preferences is improved, but the diversity of recommended titles deteriorates
Solution Approach 1:
The patent segments the recommendation process into multiple independent scoring components: relevance score (based on user consumption history), diversity score (based on genre distribution), and coverage score (based on title popularity). Each component is calculated separately and then combined through weighted summation to produce a final recommendation score, allowing independent optimization of each aspect
Solution Approach 2:
The patent introduces multiple adjustable parameters including weights for relevance, diversity, and coverage scores, as well as genre-specific parameters and time decay factors. These parameters can be modified to balance the trade-off between relevance and diversity based on specific service requirements and user behavior patterns
2Adaptability or versatility
If the service offers a wide selection of digital media content and frequent updates, then the adaptability and coverage of content interests are improved, but the difficulty of selecting and ordering relevant titles deteriorates
Solution Approach 1:
The patent pre-computes and stores various scores for each title in advance, including relevance scores based on user consumption patterns, diversity scores based on genre classifications, and coverage scores based on popularity metrics. This preliminary processing eliminates the need for complex real-time calculations when generating recommendations
Solution Approach 2:
The patent creates simplified representations of titles with pre-calculated feature vectors and scores that can be quickly compared and ranked. Instead of analyzing full title metadata and user histories in real-time, the system uses these pre-computed copies for efficient recommendation generation
Data Source
AI summary
Techniques for selecting and ordering groups of titles to present as recommendations. In one embodiment, for example, a method performed by one or more computing devices of an online services comprises selecting, for each of a plurality of row positions, a group of titles to fill the row position based at least in part on a relevance score computed for the group of titles. The relevance score is based at least in part on a personalized ranking for a particular user of titles in the groups in titles selected to fill the row positions. The groups of titles selected to fill to the row positions are presented as recommendations to the particular user as a sequence of rows in which each row in the sequence corresponds to one of the selected groups of titles.