Personalized Image Search Suggestions via Matrix Factorization
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Solution Overview
Problem
Conventional image search engines provide non-personalized search suggestions to all users, failing to account for individual user preferences and search histories, resulting in a generic user experience.
Innovation Solution
Implementing an algorithm that determines personalized image search suggestions by analyzing trending image searches, user search histories, and click-through data, using matrix factorization techniques to identify representative images that reflect user interests, and presenting these images as personalized trending image search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search engines provide the same search suggestions to all users based on general popularity, then the system complexity is low and implementation is simple, but the search suggestions lack personalization and relevance to individual user interests
Solution Approach 1:
The patent segments the search suggestion system into multiple components: a collaborative filtering module that processes user interaction data, a matrix factorization engine that generates user preference profiles, and a suggestion generation module that combines trending data with personalized profiles. This segmentation enables personalization while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by pre-computing user preference profiles through matrix factorization and pre-identifying trending search terms. These pre-computed data structures are stored and reused to generate personalized suggestions quickly, reducing the computational complexity during actual search operations.
2Measurement precision
If the system analyzes user search histories and click-through data to generate personalized suggestions, then the relevance and accuracy of search suggestions improve, but the data processing requirements and computational resources increase
Solution Approach 1:
The patent applies partial action by selecting only the most relevant features from user search histories and click-through data for matrix factorization. Instead of processing all available data, the system identifies and processes key interaction patterns, reducing computational resources while maintaining query prediction accuracy through focused analysis of the most informative data points.
3Manufacturing precision
If representative images are selected based on multiple criteria including view counts and user engagement, then the quality and relevance of displayed images improve, but the selection process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-ranking candidate images based on multiple criteria including view counts, user engagement metrics, and relevance to the user's preference profile. This pre-ranking process creates a prioritized list of candidate images, allowing the system to quickly select the top representative image without performing complex real-time evaluations, thus reducing image selection time while maintaining quality.
Data Source
AI summary
Techniques and constructs to facilitate suggestion of image-based search queries can provide personalized trending image search queries. The constructs may enable identification of trending image searches and further personalize those trending image search queries for an identified user based on information about on the user's search history and the search histories of other users. The constructs also may select a representative image for display to the user, such that selection of the representative image will execute the search query. The representative image may be selected from a plurality of candidate images based on its burstiness.