Unified User Profile for Cross-Platform Interest Capture

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Solution Overview

Problem

Conventional content recommendation systems fail to accurately capture users' interests due to fragmented user profiling, reliance on short-term interests, and inability to integrate data from diverse applications, leading to ineffective personalization and limited discovery of long-term user interests.

Innovation Solution

The system employs a universal interest space defined by public concept archives like Wikipedia to create coherent user profiles, combines short-term and long-term interest analysis, and dynamically updates content pools based on user activities and context, enabling personalized content recommendations that cater to broader user interests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If user profiling is done in isolated application settings using traditional methods, then implementation simplicity is maintained, but user interest capture accuracy deteriorates due to fragmented representation

Engineering Contradiction:

Improveuser interest capture accuracyVSAvoidsystem integration complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges user profiling across multiple applications by integrating data from diverse sources into a unified user profile. The system combines short-term and long-term interest data, aggregates user interactions from different applications, and creates a comprehensive user model that transcends application boundaries, thereby improving user interest capture accuracy while managing integration complexity through standardized data structures.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent implements a universal user profile structure that can accommodate data from multiple applications and contexts. The system uses a multi-functional framework that handles both short-term and long-term interests, supports various data sources (clicks, views, interactions), and provides a unified interface for content recommendation across different applications, enabling the system to serve multiple purposes with a single integrated architecture.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If only short-term user interests are tracked through observed activities, then responsiveness to current user behavior is improved, but understanding of persistent long-term interests deteriorates

Engineering Contradiction:

Improvecontent recommendation responsivenessVSAvoidlong-term interest understanding

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent implements preliminary action by pre-processing and storing user interaction data in both short-term and long-term buffers. The system continuously accumulates user activities, performs preliminary analysis to distinguish between transient and persistent interests, and maintains updated user profiles that reflect both immediate and enduring preferences, enabling responsive recommendations without losing long-term context.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies dynamics by implementing a dynamic user profile that adapts over time. The system continuously updates user interests based on new interactions, balances the weight between short-term and long-term interests, and allows user preferences to evolve. This dynamic approach enables the system to respond to current user behavior while preserving and learning from historical patterns of long-term interests.

Inventive Principle:

Principle #15Dynamics

3Ease of manufacture

If data is collected passively from past user behaviors, then data collection effort is minimized, but ability to discover new user interests deteriorates due to personalization bubble

Engineering Contradiction:

Improvedata collection effortVSAvoidnew interest discovery capability

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements feedback mechanisms where the system actively tests user responses to content outside their established interest profile. By analyzing user reactions to novel content recommendations, the system receives feedback that helps discover new interests. This feedback loop allows the system to gradually expand the user's personalized content universe while maintaining relevance, breaking the personalization bubble without requiring intensive active data collection.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS9535938B2Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization

Publication Date: 2017.01.03 PINTEREST INC

AI summary

A method for estimating model parameters. The method comprises receiving a data set related to a plurality of users and associated content, partitioning the data set into a plurality of sub data sets in accordance with the users so that data associated with each user are not partitioned into more than one sub data set, storing each of the sub data sets in a separate one of a plurality of user data storages, each of said data storages being coupled with a separate one of a plurality of estimators, storing content associated with the plurality of users in a content storage, where the content storage is coupled to the plurality of estimators so that the content in the content storage is shared by the estimators, and estimating, asynchronously by each estimator, one or more parameters associated with a model based on data from one of the sub data sets.