Query Completeness Classifier for Search Recommendation
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Solution Overview
Problem
Media content providers face challenges in determining whether a search query is complete or incomplete, especially in the context of 'instant search,' which affects the ability to provide accurate search query suggestions based on previous users' searches.
Innovation Solution
A classifier is trained using generated training data to predict whether a query term corresponds to a complete or incomplete query. This is achieved by comparing user-input search queries with metadata associated with selected content items, labeling queries as complete or incomplete based on the match, and using this training data to configure the classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If instant search is implemented to provide real-time search results, then user search experience is improved, but the ability to accurately determine whether a query is complete or incomplete deteriorates
Solution Approach 1:
The system performs preliminary classification of queries as complete or incomplete using a trained classifier before processing search results. This preliminary action allows the system to maintain real-time search capabilities while accurately identifying whether users have entered complete search queries, enabling subsequent tailored recommendations.
2Measurement precision
If complete queries are recommended based on previous users' searches, then search accuracy is improved, but the complexity of distinguishing complete from incomplete queries increases
Solution Approach 1:
The system uses unsupervised learning to automatically train the classifier on existing query data without requiring manual labeling. The classifier self-improves by analyzing patterns in query-completion pairs, reducing the need for complex manual configuration while maintaining high accuracy in distinguishing complete from incomplete queries.
Solution Approach 2:
The system implements feedback loops where search query completion data is continuously collected and used to retrain and improve the classifier. This feedback mechanism allows the system to adapt to new search patterns and maintain high accuracy without increasing operational complexity.
3Measurement precision
If query completion data is collected and stored for training purposes, then classifier accuracy is improved, but data privacy and security concerns increase
Solution Approach 1:
The system extracts only the necessary minimal data elements (query text and completion status) required for training the classifier, excluding any personally identifiable information. This extraction approach maintains classifier accuracy while minimizing data privacy risks by storing only what is absolutely necessary.
Data Source
AI summary
An electronic device generates training data to train a classifier to classify a respective search query as complete or incomplete, including: obtaining a first search query input by a first user; determining a media content item selected by the first user from the first search query; comparing metadata associated with the media content item with the first search query input by the first user; and labeling the first search query as complete or incomplete based on the comparison. The electronic device trains the classifier, using the generated training data, to classify a respective search query as complete or incomplete and uses the trained classifier to determine whether a second search query is complete or incomplete. The electronic device provides, for display, for a second user, one or more complete search queries as recommendations for a received search query, including the second search query if second search query is complete.