Item Title Demand Model for Search Ranking
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Solution Overview
Problem
Conventional search systems face limitations in effectively ranking query result items from item databases, particularly in content platforms where item listings have shorter lifetimes, as they primarily rely on click-based approaches that fail to accurately capture user intent and demand.
Innovation Solution
The implementation of an item title demand model that uses historical user search behavior data to generate a parameter-based model for each query, specifically the Item Skip Likelihood Estimation (ISLE) model, which determines the skip probability of tokens in item titles to rank query result items based on their demand, focusing on the token with the highest skip probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click-based approaches are used to rank query result items, then the ranking system is simple to implement, but it fails to accurately capture user intent and demand
Solution Approach 1:
The patent changes the parameter used for ranking from simple click counts to skip probabilities of tokens in item titles. By analyzing which tokens users skip when viewing search results, the system captures more nuanced information about user intent and demand, improving measurement precision while maintaining a relatively straightforward implementation approach
Solution Approach 2:
The patent introduces token skip probability analysis as an intermediary mechanism between user clicks and final ranking. Instead of directly using click data, the system analyzes token-level skip patterns in item titles to infer user demand, creating a more accurate bridge between user behavior and ranking decisions
2Adaptability or versatility
If conventional ranking methods are used, then the system works well for traditional search, but it fails in content platforms where item listings have shorter lifetimes
Solution Approach 1:
The patent implements a dynamic ranking approach that adapts to the shorter lifecycles of content platform items. By using token skip probabilities calculated from recent user behavior patterns, the system can quickly adapt to changing user demands and item relevance, making the ranking reliable even for short-lived content listings
Solution Approach 2:
The patent segments the item title into individual tokens and analyzes skip probabilities for each token separately. This segmentation allows the system to identify which specific parts of item titles are most relevant to user intent, enabling more precise and adaptable ranking for dynamic content platforms
Data Source
AI summary
Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.