Search Result Ranking With Query-Specific Appeasement Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online systems relying heavily on appeasement request data to present content may overlook items users are specifically interested in, leading to reduced user interaction rates.
Innovation Solution
An online system adjusts appeasement scores based on query specificity, using a machine learning model to predict appeasement likelihood and balance with relevance to user search queries, ensuring items displayed are both relevant and less likely to cause issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the online system heavily weights appeasement scores when presenting search results, then the quality of items and likelihood of avoiding appeasement requests improves, but user interaction rates with items users are specifically interested in deteriorates
Solution Approach 1:
The system dynamically adjusts the weight of appeasement scores based on query specificity. For generic queries, higher weight is given to appeasement scores to ensure item quality. For specific queries, lower weight is given to appeasement scores to prioritize user interest and relevance, thus resolving the contradiction between reliability and productivity
Solution Approach 2:
The ranking system transitions from a static approach (fixed weighting of appeasement scores) to a dynamic approach where weights are adjusted based on query characteristics. The system adapts its behavior according to the specificity of each search query, allowing it to optimize both item quality and user interaction rates in different contexts
2Object-affected harmful factors
If the online system uses appeasement request data to present content, then the occurrence of order issues is minimized, but the system fails to improve user interactions with content
Solution Approach 1:
The system changes the parameter of appeasement score weighting based on query specificity. By reducing the weight for specific queries, the system maintains low order issues while simultaneously improving user interactions, as users see items they are genuinely interested in rather than only items with high appeasement scores
Solution Approach 2:
Query specificity acts as an intermediary factor that mediates between appeasement scores and ranking. This intermediary allows the system to balance the reduction of order issues with the improvement of user interactions by adjusting the influence of appeasement scores based on the nature of each query
Data Source
AI summary
An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.