Location-Based Social Object Ranking for New Users
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Solution Overview
Problem
Social networking systems face challenges in recommending objects to new users with little or no interaction history, as existing recommender systems rely on user and item models that require prior interaction data, leading to inaccurate recommendations.
Innovation Solution
The system uses geographic location to recommend objects by identifying relevant pages connected to users within geographical partitions, employing a modified term frequency-inverse document frequency (tf-idf) algorithm and machine learning to predict user connections, thereby selecting and ranking locally relevant objects for new users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional recommender systems use user-user, item-item, or user-item strategies, then recommendations are accurate for users with interaction history, but they cannot generate recommendations for new users with little or no information
Solution Approach 1:
The patent segments the user base into two distinct groups: new users without interaction history and existing users with interaction history. Different recommendation strategies are applied to each segment - location-based recommendations for new users and traditional collaborative filtering for existing users, thereby resolving the contradiction between accuracy and applicability.
Solution Approach 2:
The patent introduces geographic location information as an intermediary feature that bridges the gap for new users. Instead of relying on interaction history, the system uses location data as a proxy to find similar users and generate recommendations, enabling the system to serve both new and existing users effectively.
2Measurement precision
If the system collects and processes more user interaction data, then recommendation models become more accurate, but new users cannot be served until sufficient data is accumulated
Solution Approach 1:
The patent performs preliminary actions by collecting and utilizing geographic location information immediately upon user registration, before any interaction history is accumulated. This allows the system to generate initial recommendations for new users without waiting for interaction data to accumulate, eliminating the time delay while maintaining recommendation quality.
3Adaptability or versatility
If the system uses location information to recommend objects, then new users receive relevant local recommendations, but the system complexity increases due to geographical partitioning and tf-idf calculations
Solution Approach 1:
The patent applies local quality by focusing computational resources on geographic regions relevant to each user. Instead of processing global data uniformly, the system partitions the problem space by location and applies tf-idf calculations only to locally relevant objects and users, reducing overall system complexity while maintaining the ability to serve new users effectively.
Data Source
AI summary
A social networking system recommends objects, such as pages, of the social networking system to users of the social networking system based on the location of the user. The social networking system obtains location information identifying the location of the user. Based on the location of the user, the social networking system identifies levels of geographical partitions encompassing the location of the user. For each level of geographical partitions, the social networking system accesses relevant objects of the social networking system with connections to users located within the level of geographical partitions. The social networking system may have determined a term frequency-inverse document frequency (tf-idf) value for each relevant object. Based on the number of connections and the tf-idf value associated with each relevant object, the social networking system merges the relevant objects accessed at each level into a set of relevant objects to recommend to the user.