Recommendation Engine Using Implicit User Behavior Profiles
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Solution Overview
Problem
Existing content delivery systems face challenges in recommending content to users without relying heavily on user-supplied criteria or ratings, and fail to dynamically update recommendations to reflect changing user preferences over short periods, leading to overly narrow content suggestions.
Innovation Solution
A system that uses metadata-based content records and user profiles expressed as vectors to compare and generate personalized content lists, with a digital processor calculating dot products to produce scalar quantities for content recommendation, and updates user profiles based on user actions to adapt to changing preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If filtering algorithms use collaborative or content-based filtering to generate content recommendations, then content selection accuracy is improved, but the system requires extensive user-supplied criteria and ratings which increases device complexity and user burden
Solution Approach 1:
The system automatically monitors user actions (watching, recording, fast-forwarding, pausing content) and uses these implicit behaviors to dynamically update user profiles without requiring explicit user input. The recommendation engine serves itself by extracting preferences from observed user behavior patterns rather than relying on user-provided ratings or criteria.
Solution Approach 2:
The system continuously monitors user actions on recommended and non-recommended content and uses this feedback to dynamically update user profiles. The profile update mechanism incorporates positive feedback when users engage with recommended content and negative feedback when users skip or fast-forward through content, allowing the system to adapt to changing preferences in real-time.
2Ease of manufacture
If recommendation systems rely on static user profiles based on user-entered criteria, then implementation simplicity is improved, but the system fails to adapt to changing user preferences over short periods leading to outdated recommendations
Solution Approach 1:
The system transitions from static user profiles to dynamic profiles that automatically update in real-time based on monitored user actions. The user profile is continuously refined as the system observes watching patterns, recording behaviors, and content skipping actions, allowing the recommendation system to adapt to changing preferences without requiring reconfiguration or additional user input.
Solution Approach 2:
The system automatically monitors and learns from user behavior patterns without requiring explicit user input or system reconfiguration. The profile update mechanism operates autonomously by extracting preferences from observed actions such as watching complete programs, recording content for later viewing, or fast-forwarding through content, enabling the system to self-adapt to changing user preferences.
3Measurement precision
If recommendation playlists are updated based on user explicit feedback and implicit actions, then recommendation accuracy is improved, but the playlists become too narrowed and specific losing versatility
Solution Approach 1:
The system adjusts the weighting parameters in user profiles based on the type of user action observed. Different actions (watching, recording, fast-forwarding, pausing) contribute differently to profile updates, with positive reinforcement for engagement actions and negative reinforcement for skipping actions. This parameter adjustment allows the system to maintain accuracy while preserving content variety by balancing specialization with exploration.
Data Source
AI summary
Recommendation engine apparatus and associated methods provide content compiled from various sources and selected to match user preferences. In one embodiment, the recommendation apparatus comprises a headend entity; in another, it is co-located on a user's CPE. In one embodiment, the recommendation engine creates content records from content metadata for comparison to a user profile. The user profile is pre-programmed; however has the ability to dynamically shift toward a user's preferences as the user takes actions regarding content. Client applications are utilized to compile and present content; feedback mechanisms are utilized to enable “learning” from user activities to generate more precise recommendations as well as to “unlearn” stale preferences. Recommended content is displayed in the form of a playlist, or as a continuous stream on a virtual channel, or presented in an electronic program guide. A business rules “engine” useful in implementing operational or business goals is also disclosed.