Friend Impact Prediction Model for App Recommendation
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Solution Overview
Problem
Current application recommendation methods, such as ranking based on download counts, are undiversified and fail to account for changing user needs, leading to a lack of variety in recommended applications and difficulties for new users with insufficient experience data to make informed recommendations.
Innovation Solution
A method and apparatus that utilize a pre-trained friend impact prediction model to determine the influence of a user's social interactions on application registration, calculating a recommendation score based on the impact of friends on the target user's application usage, thereby recommending applications based on social relationships rather than solely on popularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If application recommendation is based on downloading counts, then popular applications can be found quickly, but the recommendation diversity and adaptability to changing user needs deteriorates
Solution Approach 1:
The patent transforms the static recommendation approach into a dynamic one by introducing time-dependent factors. The patent calculates a time factor based on the registration time of friends and the current time, and uses this to dynamically adjust the recommendation score. This allows the system to adapt to changing user needs over time while still considering popular applications, thereby resolving the contradiction between finding popular apps quickly and adapting to changing needs.
Solution Approach 2:
The patent changes the parameters used for recommendation from solely downloading counts to a composite scoring system that includes multiple dimensions: downloading count factor, friend impact factor, and time factor. By changing these parameters and their weights dynamically, the system can balance between recommending popular applications and adapting to diverse user needs, including those of new users without extensive download history.
2Measurement precision
If application recommendation relies on user experience data, then personalized recommendations improve, but new users with insufficient data cannot receive accurate recommendations
Solution Approach 1:
The patent introduces friends as an intermediary to bridge the gap for new users. Instead of relying directly on the target user's insufficient experience data, the system uses the download behavior and interaction data of friends (particularly those registered within a certain time window) as proxy information. This intermediary approach allows new users to receive personalized recommendations without needing extensive personal experience data, while still maintaining recommendation precision through the friend influence model.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing the friend impact prediction model and various factors (download count factor, time factor) before actual recommendation needs arise. This allows the system to quickly generate accurate recommendations for new users as soon as they register, without requiring them to accumulate sufficient personal experience data first. The preliminary preparation of models and factors enables immediate personalized recommendation capability.
3Adaptability or versatility
If recommendation system considers social interactions, then recommendation diversity improves, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation problem into distinct modular components: download count factor calculation, friend impact prediction model, time factor calculation, and final score aggregation. Each component is independently calculated and then combined. This segmentation reduces system complexity by making each part manageable and independently optimizable, while still achieving diverse and adaptive recommendations through their integration.
Data Source
AI summary
A method, apparatus and server for recommending an application are provided. The method may include determining at least one target friend among a plurality of friends of a target user, obtaining social interaction characteristics between the target user and the at least one target friend, based on a pre-trained friend impact prediction model of a target application and the social interaction characteristics between the target user and the at least one target friend, determining a first impact of the at least one target friend on registration of the target user in the target application, determining a recommendation score corresponding to the target user and the target application according to the first impact of the at least one target friend on registration of the target user in the target application, and performing application recommendation according to the recommendation score.