Long-Tail Topic Scoring for Personalized Content Delivery
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Solution Overview
Problem
Current methods fail to effectively characterize and provide content based on users' long-tail interests, which are unique and important but not addressed by existing personalization techniques.
Innovation Solution
A system and method that utilizes a user profile with long-tail topic scores to identify and update long-tail content, incorporating a content search/recommendation engine, long-tail interest content retriever, and tracker to dynamically adapt user profiles based on online activities, ensuring personalized content delivery that includes both popular and long-tail interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current personalization techniques focus on popular interests shared by many users, then content can be provided to a mass volume of users, but long-tail interests of individual users cannot be effectively characterized or addressed
Solution Approach 1:
The patent segments user interests into two distinct categories: popular interests (shared by many users) and long-tail interests (unique to individual users). This segmentation is achieved by analyzing user profile data and identifying interests that appear frequently across multiple users versus those that are rare and user-specific. The system then applies different content recommendation strategies for each segment, enabling mass content delivery for popular interests while simultaneously providing personalized long-tail content for individual users.
Solution Approach 2:
The patent introduces a new dimension to user profiling by adding a 'long-tail interest' dimension alongside traditional popular interest dimensions. This is accomplished by extending the user profile structure to include rare interest tags and by implementing a multi-dimensional interest space where both common and unique interests can coexist. The system navigates this extended dimensionality to retrieve and recommend content that matches users' long-tail interests without sacrificing the ability to serve popular content at scale.
2Reliability
If user profiles are constructed based on overlapping popular interests, then confidence in estimated interests increases, but unique long-tail interests are lost or overlooked
Solution Approach 1:
The patent extracts long-tail interests from the aggregate user profile data by identifying and isolating rare interest patterns that do not overlap with popular interests. This extraction process involves filtering user profile information to separate common interests (shared across multiple users) from unique interests (present in only one or few user profiles). The extracted long-tail interests are then stored and managed separately, ensuring they are not lost in the aggregation process and can be used for personalized content recommendation.
Solution Approach 2:
The patent creates a composite user profile structure that combines both popular interest data and long-tail interest data into a unified profile model. This composite profile maintains the reliability benefits of popular interest aggregation while incorporating the uniqueness of long-tail interests. The system uses weighted combinations of different interest types, where popular interests provide a foundation of reliable preferences and long-tail interests add personalized nuance, resulting in a more complete and accurate representation of user preferences.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for content serving. A user's profile characterizing the user's long-tail interest with respect to some long-tail topics may be obtained. Each long-tail topic in the user's profile is associated with a long-tail topic score representing a degree of the user's interest in the long-tail topic. Long-tail content in some long-tail topics may be identified for the user and sent to the user. When information about online activities of the user directed to the long-tail content is received, corresponding long-tail topic scores in the user profile associated with the long-tail topics are updated based on the user's online activities.