Push Object Selection Using Multi-Dimensional Matching to Reduce Overhead
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Solution Overview
Problem
Existing internet platforms face high communication resource overheads due to pushing objects with low matching degrees to users, as current recommendation methods are based on monotonous dimensions.
Innovation Solution
A push object processing method that involves obtaining interaction object and user information, determining user and object features, and selecting candidate push objects based on these features using a trained object push model to enhance matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If objects are pushed to users based on monotonous dimension (historical interaction), then the recommendation process is simple, but the matching degree between pushed objects and users is low causing high communication resource overheads
Solution Approach 1:
The patent transitions from one-dimensional historical interaction-based recommendation to multi-dimensional recommendation by incorporating user description features (age, gender, location) and object features (category, tags, content). This dimensional expansion enables more accurate matching while the system optimizes communication resources by pre-filtering and selecting only the most relevant objects for push, thereby reducing unnecessary communication overheads.
2Adaptability or versatility
If more objects are pushed to users to ensure coverage, then the user experience is improved, but the communication resource overheads increase due to low matching degree objects
Solution Approach 1:
The patent changes the parameters used for object selection by introducing user description features and object features as additional filtering criteria. Instead of pushing objects based solely on historical interaction, the system uses these features to calculate matching degrees and select objects that best match user profiles, thereby improving user experience while reducing the quantity of objects needing to be communicated.
Solution Approach 2:
The patent replaces the mechanical approach of pushing all historical interaction objects with an intelligent filtering mechanism that uses feature-based matching. The system substitutes brute-force pushing with a sophisticated selection process that calculates matching degrees between user features and object features, retaining only the most relevant objects for push and thus reducing communication resource overheads.
3Loss of energy
If feature-based matching is implemented to improve matching degree, then communication resource overheads are reduced, but the system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct modules: user description feature extraction module, object feature extraction module, matching degree calculation module, and object selection module. This segmentation allows each module to handle specific tasks independently, making the overall complex system more manageable and easier to implement. The user features and object features are extracted separately and then combined through matching degree calculations.
Data Source
AI summary
In a push object processing method, interaction object information corresponding to a target user is obtained. The interaction object information includes information of at least one object having an interaction relationship with the target user. A user description feature of the target user is determined based on the interaction object information. Interaction user information corresponding to a plurality of to-be-pushed objects is obtained. The interaction user information of each of the plurality of to-be-pushed objects includes information of at least one user having an interaction relationship with the respective to-be-pushed objects. Based on the interaction user information, object features corresponding to the plurality of to-be-pushed objects are determined. A candidate push object from the plurality of to-be-pushed objects is selected based on the user description feature and the object features.