Real-Time Interest Calculation for Information Recommendation
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Solution Overview
Problem
Existing information recommendation systems lack timeliness in recommending real-time information, such as news, due to their reliance on large user participation and failure to consider changes in user interests, resulting in poor recommendation effectiveness for one-time consumption content.
Innovation Solution
A method that calculates a user's short-term, long-term, and real-time interests by analyzing user behavior data, using daily and long-term interest weights attenuated over time, and current interest weights to recommend relevant real-time information, incorporating factors like timeliness and click-through rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation algorithms (behavior-based or content-based) are used, then information recommendation can be provided, but timeliness is poor and cannot capture real-time user interests
Solution Approach 1:
The patent segments user interest into three distinct time dimensions: long-term interest (stable preferences), short-term interest (recent behavioral patterns), and real-time interest (current click behavior). This segmentation allows the system to process and weigh different types of user interests separately, enabling accurate real-time recommendations without being constrained by the limitations of traditional unified recommendation algorithms.
Solution Approach 2:
The patent implements dynamic interest weighting where the system continuously updates user interest profiles based on real-time click behavior. The real-time interest component is dynamically integrated with long-term and short-term interests, allowing the recommendation system to adapt to changing user preferences immediately rather than relying on static or periodically updated user profiles.
2Quantity of substance
If traditional recommendation systems rely on large user participation for statistical analysis, then recommendation coverage can be improved, but response speed to real-time information decreases
Solution Approach 1:
The patent extracts the real-time interest component from the traditional behavior-based recommendation framework and processes it separately with higher priority. By taking out the real-time click behavior analysis from the bulk statistical processing of user behaviors, the system can immediately respond to real-time information while still utilizing aggregated user participation data for long-term and short-term interest modeling.
Solution Approach 2:
The system performs preliminary classification of user behaviors into different time dimensions (long-term, short-term, real-time). This preliminary action allows the system to pre-process and prioritize real-time click data, enabling faster response to real-time information without waiting for complete statistical analysis of all user participation data.
3Device complexity
If existing algorithms treat all user behaviors uniformly, then processing simplicity is maintained, but ability to capture changing user interests is lost
Solution Approach 1:
The patent applies local quality by assigning different weights and processing methods to different time dimensions of user interest. Rather than treating all user behaviors uniformly, the system gives special emphasis to real-time click behavior while still incorporating long-term and short-term interests with appropriate weighting. This localized differentiation enables the system to capture changing user interests without requiring complete redesign of the entire recommendation algorithm.
Data Source
AI summary
A method for recommending real-time information includes: obtaining user behavior data of a user; based on the user behavior data, respectively calculating a short-term interest, a long-term interest, and a real-time interest of the user; determining an interest of the user according to the short-term interest, the long-term interest, and the real-time interest of the user; and recommending real-time information to the user based on the interest of the user.