Interactive Content Recommender Using Dynamic Dialog Adaptation
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Solution Overview
Problem
Existing content recommendation systems fail to accurately account for changes in user preferences and unpremeditated user choices, leading to suboptimal content suggestions.
Innovation Solution
The implementation of interactive humanoid conversational entities, such as virtual agents or chatbots, that utilize a weighted algorithm to build a content recommendation model based on user interactions, including explicit and implicit feedback, navigational paths, mood, and social network interactions, to provide personalized content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive technology incorporates historical user preferences to recommend content, then content recommendation accuracy is improved, but the system fails to account for changes in user taste and unpremeditated user choices
Solution Approach 1:
The patent implements a dynamic recommendation system that transitions from static historical preference analysis to an interactive dialog-based approach. The system continuously adapts to user feedback during the conversation, allowing real-time adjustment of recommendations based on changing user tastes and unpremeditated choices, thereby resolving the contradiction between maintaining accuracy and adapting to changes.
Solution Approach 2:
The system incorporates explicit user feedback through dialog interactions where users can correct, refine, or redirect recommendations. This feedback mechanism allows the system to learn from user responses and adjust its recommendations dynamically, ensuring both accuracy and adaptability to evolving user preferences.
2Adaptability or versatility
If dialog processes solicit multiple inputs from users to improve recommendations, then recommendation personalization is improved, but interaction complexity increases
Solution Approach 1:
The system performs preliminary actions by proactively presenting structured attribute options to users before they need to make detailed selections. By anticipating user needs and providing curated choices upfront, the system reduces the cognitive load and interaction complexity while still gathering sufficient information for personalized recommendations.
Solution Approach 2:
The recommendation process is segmented into discrete attribute dimensions (e.g., genre, mood, format) that are explored sequentially through dialog. This segmentation allows the system to collect personalized information systematically without overwhelming the user, breaking down complex personalization into manageable interaction steps.
Data Source
AI summary
Methods and systems including computer programs encoded on a computer storage medium, for interactive content recommendation. In one aspect, a method includes receiving a request for content by a user, determining a user intent based on the received request, providing to the user a first attribute responsive to the user intent, receiving a first attribute value responsive to the first attribute, providing a second attribute, and receiving a second attribute value responsive to the second attribute. A particular content vector including a first content attribute and a second content attribute for a particular content item is identified where the first content attribute and the second content attribute sufficiently match the first attribute value and the second attribute value. The particular content item is provided as a suggested content item, and, responsive to a user selection of the particular content item, provided for presentation on the user device.