Geographic Interest Profile for Cold Start Recommendations
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Solution Overview
Problem
Content-aggregation websites face challenges in serving relevant content to new or infrequent users due to empty or nearly empty personalized user-interest profiles, known as the 'cold start' problem, where existing solutions fail to effectively generate interest profiles for such users.
Innovation Solution
A processor-executed method generates a baseline interest profile for a geographical location by deriving entities from a corpus of documents, associating them with expected values based on engagement signals and using a sparse-polarity approach to create rankings of ingested articles, which are then served to new or infrequent users based on similarity measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering is used to generate personalized recommendations, then recommendation accuracy is improved for existing users, but the system fails for new or infrequent users with empty profiles
Solution Approach 1:
The patent segments the recommendation problem into two distinct approaches: collaborative filtering for existing users with populated profiles, and geographic-based content analysis for new or infrequent users with empty profiles. This segmentation allows each method to be optimized for its specific use case, resolving the contradiction between accuracy for existing users and applicability to new users.
Solution Approach 2:
The patent introduces geographic location data and entity-based content analysis as an intermediary approach for new users. Instead of directly applying collaborative filtering to empty profiles, the system uses geographic information and content entity analysis as a bridge to generate initial recommendations, which can then feed into the collaborative filtering system as users become more active.
2Measurement precision
If geographic location data is used to generate baseline interest profiles, then recommendations for new users are improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-computing entity representations and aboutness scores for all ingested content before new users arrive. Geographic interest profiles and content entity vectors are prepared in advance, allowing the system to quickly generate recommendations for new users without performing complex computations at the time of user registration, thus managing system complexity.
Solution Approach 2:
The patent uses copying by creating vector representations of geographic interest profiles and content entities. Instead of storing and processing raw geographic and content data directly, the system creates simplified vector copies that capture the essential information, reducing computational complexity while maintaining recommendation quality.
3Measurement precision
If sparse-polarity weighting is applied to engagement signals, then discriminative power of interest profiles is improved, but computational processing time increases
Solution Approach 1:
The patent applies sparse-polarity weighting in advance during the pre-computation phase to generate entity representations and geographic interest profiles. By performing this computationally intensive weighting operation beforehand, the system achieves high discriminative power in its interest profiles while avoiding the time penalty during actual recommendation generation for new users.
Data Source
AI summary
Software for a content-aggregation website generates a first representation of interests for a geographical location. The representation includes a plurality of entities that are derived from a corpus of documents. Each of the plurality of entities is associated with an expected value that is based on engagement signals from users in the geographical location and that is weighted using a sparse-polarity approach to be discriminative with respect to other entities. Each of the ingested articles is represented by the second representation that associates an aboutness score with each of the plurality of entities. The software uses the first representation, a similarity measure, and a second representation to create rankings of a plurality of ingested articles received. Then the software receives a request for access to the content-aggregation service from a new user from the geographical location and serves the new or infrequent user a content stream based on the rankings.