Recommender System Profile-Based Targeted Query Generation
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Solution Overview
Problem
Recommender systems face inefficiencies and scalability issues when filtering and rating content items from large distributed databases, requiring access to all items for generating recommendations, which is not feasible.
Innovation Solution
Employing a user profile to generate targeted queries for content sources, using feature-value pairs to select the most discriminative features and compose queries, allowing for efficient recommendation generation without needing complete access to all database items, utilizing algorithms like Relief for feature selection and iterative query refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the recommender system accesses all items in large distributed databases to generate recommendations, then the completeness and accuracy of recommendations is improved, but the system complexity and computational resources required become unmanageable
Solution Approach 1:
The patent extracts and utilizes metadata from content items instead of accessing complete item data. The metadata extraction mechanism selectively retrieves only the necessary descriptive information (genres, actors, directors, etc.) required for recommendation generation, leaving the bulk of the database inaccessible and thus reducing system complexity while maintaining recommendation accuracy.
Solution Approach 2:
The system segments the recommendation process into distinct components: metadata extraction, user profile matching, and recommendation generation. This segmentation allows each component to operate independently with optimized resources, avoiding the need to load and process entire database items in memory simultaneously.
2Measurement precision
If the recommender system filters and rates all content items from distributed databases, then the relevance of recommendations is improved, but the processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary filtering by matching user profile characteristics with content metadata before accessing full item details. This preliminary action identifies only those content items that are potentially relevant to the user, eliminating the need to process and evaluate all items in the distributed databases, thus reducing processing time while maintaining recommendation relevance.
Solution Approach 2:
The patent applies partial action by processing only a subset of content items that match user profile criteria rather than all items. The system performs excessive filtering at the metadata level to ensure no relevant items are missed, then focuses computational resources only on the filtered subset for detailed evaluation.
3Ease of operation
If the system uses implicit learning from user actions to generate recommendations, then the user experience is improved by freeing users from explicit rating, but the interpretation complexity of user actions increases
Solution Approach 1:
The system implements self-service by automatically observing and interpreting user actions (purchases, downloads, playback selections, deletions) without requiring explicit user input. The recommender autonomously translates these actions into preference signals and generates recommendations, improving user convenience while managing interpretation complexity through automated rule-based translation mechanisms.
Data Source
AI summary
The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a user profile normally used by a recommender to predict user ratings is employed to generate a targeted query for the remote database yielding a set of results that can be scored by the recommender and provided as suggestions to the user.