Recommender Distribution Tuning for Frequency-Constrained Item Exposure
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Solution Overview
Problem
Existing recommender systems often fail to recommend information that aligns with user preferences beyond their direct interests, leading to inefficiencies and exposure to irrelevant or harmful content, and lack the ability to dynamically adjust the frequency of item recommendations.
Innovation Solution
A method that involves generating a binary recommendation array based on initial scores, applying an objective function, and constraints to optimize the recommendation process, ensuring alignment with user preferences and desired frequencies, using techniques like linear programming or convex optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recommender systems closely trace user preferences based on past interactions, then recommendation relevance to user history is improved, but the system fails to recommend information in categories where user shows no preference, exposing users to harmful or false information
Solution Approach 1:
The system changes the parameter of recommendation distribution by introducing a tunable parameter that controls the balance between exploring user preferences and discovering new categories. This allows the system to dynamically adjust recommendation behavior to achieve both relevance and diversity.
Solution Approach 2:
The patent implements dynamic recommendation distribution by allowing the recommendation system to adapt its behavior over time. The system can shift between focusing on user preferences and exploring new categories based on configurable parameters, making the recommendation process dynamic rather than static.
2Productivity
If manual rules are applied along with recommendation algorithms to control item frequency, then some frequency control is achieved, but the system performance becomes sub-optimal and users are exposed to irrelevant information
Solution Approach 1:
The patent replaces manual mechanical rules with an automated optimization system. Instead of using fixed manual thresholds or rules to control recommendation frequency, the system uses an optimization algorithm that automatically determines optimal recommendation frequencies based on user preferences and item characteristics, eliminating the need for manual intervention and improving both quality and control.
3Ease of manufacture
If existing recommender systems use fixed recommendation algorithms, then implementation is simple, but the system cannot dynamically adjust the frequency of item recommendations based on changing user preferences or business goals
Solution Approach 1:
The system introduces dynamic adjustability while maintaining a relatively simple implementation by using an optimization framework that can be configured through parameters rather than complex reprogramming. The optimization algorithm can adapt to changing user preferences and business goals by adjusting configurable parameters, providing dynamic frequency adjustment capability without significantly increasing system complexity.
Data Source
AI summary
The present disclosure provides techniques for automatically recommending information with a tunable recommendation distribution. One example method includes receiving, for a plurality of users, recommendation scores of a plurality of items generated using a recommender system based on one or more metrics, generating a binary recommendation array based on the recommendation scores, receiving an objective function associated with the recommendation scores and the binary recommendation array, receiving a set of constraints associated with the plurality of users or the plurality of items, wherein the set of constraints comprises a target recommendation frequency range for an item of the plurality of items, updating the binary recommendation array based on evaluating the objective function subject to the set of constraints, and recommending, to each user of the plurality of users, one or more items of the plurality of items based on the updated binary recommendation array.