Search Result Ranking With Cold-Start Conversion Scoring
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Solution Overview
Problem
Existing search engines and recommendation systems face challenges in providing relevant results for new or previously unseen queries or items, known as the cold-start problem, due to a lack of sufficient data or interaction history, leading to these items being less likely to appear in search results.
Innovation Solution
An online system identifies a candidate set of cold start results by filtering based on relevance features and uses a conversion prediction model that predicts a probability of conversion without user interaction data, enabling a scoring baseline for ranking these results alongside standard results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a scoring model uses user interaction data to rank search results, then ranking accuracy for standard results is improved, but cold start results are disadvantaged and less likely to appear
Solution Approach 1:
The patent segments the scoring process into two independent components: a relevance score based on query-item matching features, and a popularity score based on interaction data. Cold start results receive a default popularity score, allowing them to compete fairly with standard results that have interaction histories. This segmentation resolves the contradiction by enabling accurate ranking through relevance while ensuring cold start results are not disadvantaged by lack of interaction data.
Solution Approach 2:
The patent changes the parameter weighting dynamically based on result type. For cold start results, the relevance score is weighted more heavily while the popularity score uses a default value. For standard results, both relevance and popularity scores are weighted. This parameter change allows the system to maintain high ranking accuracy for standard results while ensuring cold start results achieve adequate visibility.
2Measurement precision
If the system prioritizes results with interaction histories, then personalization accuracy is improved, but new items receive insufficient exposure
Solution Approach 1:
The patent applies partial action by using interaction data selectively - only for results that have interaction histories. Cold start results receive a default popularity score rather than no score, ensuring they get sufficient exposure. This partial use of interaction data maintains personalization accuracy for established items while preventing complete exclusion of new items.
Solution Approach 2:
The patent introduces an intermediary mechanism - the dual-component scoring system with default popularity scores - that mediates between personalization accuracy and new item exposure. This intermediary allows the system to prioritize personalized results while ensuring new items receive adequate exposure through the relevance score component.
3Ease of operation
If the system uses engagement-based ranking, then user experience is improved, but the cold-start problem persists causing relevant new items to be hidden
Solution Approach 1:
The patent segments the ranking into relevance scoring and popularity scoring, allowing new items to compete on relevance without being penalized for lack of engagement data. This segmentation maintains user experience through accurate relevance matching while preventing loss of information about new items.
Solution Approach 2:
The patent applies preliminary action by assigning default popularity scores to cold start results before any user interaction occurs. This preliminary scoring ensures new items have a baseline visibility level, preventing them from being completely hidden while the system gathers interaction data for future personalization.
Data Source
AI summary
An online system receives a query from a user of the online system. The online system identifies a candidate set of cold start results to the query defined as having been presented to the user less than a threshold number of times. The cold start results are then filtered based on their relevance to the query to generate a final set of cold start results and a score is generated for each cold start result without interaction data using a scoring baseline common to standard results with interaction data. Accordingly, the online system ranks the cold start results with a set of standard results based on the score for each cold start result using the scoring baseline and presents the same for display to the user.