Entity Type Search Indexes for Intent Prediction
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Solution Overview
Problem
Online systems that store large amounts of objects face resource-intensive challenges in processing search queries, leading to prolonged processing times and negative user experiences, especially when relevant objects are not prioritized in search results.
Innovation Solution
Implementing a machine learning-based approach that uses search indexes to predict the entity type of objects by extracting term features from search queries and feeding them into a machine learning model, such as a random forests model, to limit searches to relevant entity types and rank results accordingly, thereby reducing resource consumption and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the online system performs individual determination for each stored object to identify relevant objects, then the search results can include relevant objects, but the process consumes a large amount of system resources and takes a long period of time
Solution Approach 1:
The patent segments the search process into two phases: (1) pre-computation of entity type probabilities using machine learning models trained on historical data, and (2) runtime search that leverages these pre-computed probabilities to filter and rank objects. This segmentation allows the system to prepare search capabilities in advance, reducing real-time processing requirements while maintaining accurate relevance determination.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical search data to learn entity type distributions and object characteristics. These pre-computed entity type probabilities and learned patterns are stored and reused during runtime searches, eliminating the need to perform full individual object analysis for every search query while preserving search accuracy.
2Quantity of substance
If the online system stores a large amount of objects, then the system provides comprehensive data access, but the process of identifying related objects consumes large amounts of system resources
Solution Approach 1:
The patent extracts and utilizes metadata features from objects (such as entity type indicators, categories, and descriptive attributes) to create compact representations that enable efficient filtering. Instead of analyzing full object content for every search, the system extracts key features and uses machine learning models to work with these condensed representations, significantly reducing computational resources required while maintaining search effectiveness across large object collections.
Solution Approach 2:
The system changes parameters by transforming raw object data into feature vectors and probability distributions that are more efficient for computational processing. Machine learning models learn optimal parameter representations from historical data, converting complex object attributes into condensed numerical features that enable faster comparison and matching during searches, thereby reducing resource consumption while handling large quantities of objects.
3Reliability
If the most relevant objects are not included at the top of search results, then all related objects can be returned, but the user has a negative experience with the online system
Solution Approach 1:
The system implements feedback mechanisms by analyzing user interactions with search results (such as clicks, selections, and refinement actions) to continuously improve the ranking model. Historical search data is fed back into the machine learning system to retrain and refine entity type probability predictions, ensuring that the most relevant objects consistently appear at the top of results. This feedback loop maintains both result completeness and user satisfaction by learning from actual user behavior patterns.
Data Source
AI summary
An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.