Topic Subscription System Matching Persistent Topics to User Interests
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Solution Overview
Problem
Existing methods for subscribing to topics in Internet technology fail to accurately represent users' interests, as they do not consider individual attributes or behaviors, leading to a low probability of hitting relevant content.
Innovation Solution
A method and apparatus that match persistent topics based on user input combined with historical behavior or subscription records, allowing for real-time adjustment of recommendations to better align with user interests, including a topic matching module, display module, and resource recommendation module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resource-based recommendation methods are used to subscribe to topics, then the system can provide implicit recommendations based on user clicks, but the granularity of user needs is too coarse and the probability of hitting real user needs is low
Solution Approach 1:
The patent segments user interests into multiple dimensions including explicit preferences, implicit preferences, and contextual factors. Instead of treating user interests as a single coarse-grained entity, the system divides them into distinct components that can be independently analyzed and combined to form a comprehensive user profile, thereby improving the precision of user interest representation.
Solution Approach 2:
The patent introduces additional dimensions for representing user interests beyond simple click behavior. It incorporates explicit user preferences, implicit preferences derived from interaction patterns, and contextual information as separate dimensional layers. This multi-dimensional approach transforms the coarse-grained single-dimension representation into a fine-grained multi-dimensional model, enhancing both adaptability and precision.
2Ease of operation
If subscription need based recommendation methods are used, then users can search and subscribe to topics of interest, but user attributes and behaviors are not considered leading to inaccurate interest representation
Solution Approach 1:
The patent merges multiple sources of user information including explicit preferences, implicit preferences from behavior patterns, and contextual attributes into a unified user profile. This combination integrates the ease of operation of subscription-based methods with the precision of behavior-based analysis, creating a comprehensive representation that maintains both user-friendly subscription capabilities and accurate interest modeling.
Solution Approach 2:
The patent implements feedback mechanisms that continuously update user profiles based on interaction patterns. User attributes and behaviors are monitored and fed back into the recommendation system to refine the precision of user interest representation over time, while maintaining the ease of operation through persistent subscription capabilities.
3Adaptability or versatility
If topic subscription is based on entity words or media public accounts, then users can subscribe to topics, but the method is too dependent on media accounts and cannot continuously hit user needs
Solution Approach 1:
The patent transforms the static subscription model based on fixed entity words or media accounts into a dynamic system that continuously adapts to user needs. User profiles are updated in real-time based on interaction patterns, allowing the system to dynamically adjust recommendations while maintaining subscription capabilities. This dynamic approach ensures continuous reliability in satisfying user needs without over-dependence on specific media accounts.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for subscribing to a topic. The method includes: matching a persistent topic for a retrieval keyword based on the retrieval keyword of a user combined with at least one of historical behavior or a subscription record of the user; returning the persistent topic to a client for display, so that the user performs subscription; and saving the persistent topic subscribed to by the user, and when a matching resource corresponding to the persistent topic subscribed to by the user is updated, recommending the updated matching resource to the user. Since a recommendation strategy can be timely adjusted according to real-time behavior of a user combined with historical behavior, the probability of hitting a topic of interest to the user is increased.