Conversational Multimedia Recommendation Using Authorized Interaction Data

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Solution Overview

Problem

Existing multimedia content recommendation systems fail to comprehensively explore user interests beyond preference information and historical browsing data, leading to limited and inaccurate content recommendations.

Innovation Solution

Integrate an intelligent agent, such as a machine learning model, into the recommendation stream, utilizing historical interaction data from conversations to determine multimedia content that aligns with user interests, enhancing recommendation accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If recommendation systems use only preference information and historical browsing data, then the system complexity remains low, but the recommendation accuracy and comprehensiveness of user interest exploration is insufficient

Engineering Contradiction:

Improverecommendation accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent combines multiple data sources including preference information, historical browsing data, and conversational interaction data into a unified recommendation system. The agent module integrates these diverse data types to comprehensively explore user interests, thereby improving recommendation accuracy without creating excessive system complexity through cohesive data fusion.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces an agent module as an intermediary component that processes and analyzes conversational interaction data. This agent acts as a mediator between raw conversation data and the recommendation output, extracting meaningful patterns and translating them into improved recommendation decisions, thus enhancing accuracy while maintaining manageable system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If the system integrates conversational interaction data from agents, then the comprehensiveness of user interest exploration improves, but the data processing complexity and computational requirements increase

Engineering Contradiction:

Improvecomprehensiveness of user interest explorationVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the data processing task by separating conversational data analysis into a dedicated agent module. This segmentation allows specialized processing of conversational interactions distinct from traditional browsing data processing, enabling comprehensive user interest exploration while distributing computational complexity across dedicated processing components rather than monolithic systems.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameter space for user interest analysis by incorporating conversational interaction data alongside traditional browsing behavior data. This parameter expansion allows the system to capture nuanced user interests through conversation patterns, improving comprehensiveness while managing data processing complexity through targeted analysis of conversational parameters.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If historical interaction data from multiple agents is utilized, then the personalization of content recommendations improves, but the data privacy concerns and authorization requirements increase

Engineering Contradiction:

Improvepersonalization precisionVSAvoiddata privacy concerns

Core Design Contradiction:

Manufacturing precisionVSObject-affected harmful factors

Solution Approach 1:

The patent implements a feedback mechanism where users authorize the system to access and utilize their historical interaction data from conversations with agents. This explicit user feedback and authorization ensure that data is collected and processed only with user consent, addressing privacy concerns while enabling precise personalization through authorized data utilization.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies local quality by processing and analyzing conversational interaction data specifically from agents that users have authorized, rather than uniformly processing all available data. This targeted approach to data processing enhances personalization precision for authorized data sources while minimizing privacy risks by limiting data access to user-approved contexts.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250337706A1Multimedia content recommendation method and device, electronic device and storage medium

Publication Date: 2025.10.30 BEIJING ZITIAO NETWORK TECH CO LTD

AI summary

The present disclosure relates to a multimedia content recommendation method and device, an electronic device and a storage medium. The multimedia content recommendation method includes: displaying a first entrance of a recommendation stream of multimedia content in a conversation interface between a user and a first agent; displaying a playing interface of the recommendation stream in response to a trigger operation on the first entrance; determining a multimedia content recommended for the user based on historical interaction data authorized by a user between the user and at least one of the first agent or an agent other than the first agent; and displaying the multimedia content recommended for the user in a playing interface of the recommendation stream.