Long-Term Objective Content Recommendation via LLM Alignment

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Solution Overview

Problem

Existing systems struggle to effectively recommend non-text content items, such as images, from large corpora to users, often relying on immediate interaction rather than long-term objectives.

Innovation Solution

The system uses a Large Language Model (LLM) to generate captions for selected content items and process them to identify recommended content items based on long-term objectives, such as cumulative engagement, by employing reverse inference learning and alignment scores.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional recommendation systems are used to recommend content items, then immediate user interaction is encouraged, but long-term engagement objectives are not optimized

Engineering Contradiction:

Improveuser engagementVSAvoidlong-term objective achievement

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent inverts the traditional recommendation approach by using reverse inference learning. Instead of predicting user behavior forward in time, the system infers recommendations backward from long-term engagement objectives, fundamentally changing the temporal direction of the recommendation logic to prioritize long-term goals over immediate interactions

Inventive Principle:

Principle #13The other way round (Inversion)

2Productivity

If content items are recommended based on immediate interaction, then short-term engagement is improved, but cumulative long-term engagement is not optimized

Engineering Contradiction:

Improveshort-term engagementVSAvoidlong-term engagement duration

Core Design Contradiction:

ProductivityVSDuration of action of stationary object

Solution Approach 1:

The system performs preliminary action by pre-computing alignment scores that evaluate how well each content item aligns with long-term engagement objectives. These alignment scores are calculated in advance and stored, allowing the recommendation system to make informed decisions that prioritize long-term engagement without sacrificing short-term responsiveness

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If a large corpus of content items is maintained, then content variety is increased, but determining recommended items becomes more difficult

Engineering Contradiction:

Improvecontent varietyVSAvoidrecommendation determination complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical filtering and sorting mechanisms with a machine learning-based alignment score computation system. The LLM automatically evaluates content items against long-term objectives, substituting manual or rule-based recommendation determination with an intelligent system that scales efficiently even as corpus size increases

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250139683A1Recommending content items based on a long-term objective

Publication Date: 2025.05.01 PINTEREST INC

AI summary

Described are systems and methods for implementing a recommendation system that is configured and/or optimized to determine recommended content items based on a long-term objective. The exemplary recommendation system may be generated based on a mapping between content items and the long-term objective. The mapping between the content items and the long-term objective may be determined based on mappings utilizing various interim metrics, which may be determined using trained models configured to predict a respective target variable based on respective inputs. The mapping may also be generated using alignment scores and/or a large language model.