Customized Autocomplete Cache Update via User Similarity
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Solution Overview
Problem
Existing autocomplete systems provide generic suggestions that may not align with individual user interests or behaviors, leading to inefficiencies in user input due to the need for correcting erroneous keystrokes and the time-consuming nature of text entry.
Innovation Solution
A computing device generates customized autocomplete suggestions by analyzing profile data from similar users, selecting relevant search queries based on similarity scores, and updating a local autocomplete cache to reflect current trends and user similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic autocomplete data is used, then the autocomplete suggestions are broadly applicable, but the relevance to individual user interests and behaviors is low
Solution Approach 1:
The patent segments the autocomplete data system into multiple components: a global autocomplete cache for broad applicability, user-specific profile data for personalization, and dynamically generated updates tailored to individual users. This segmentation allows the system to provide both generic and customized suggestions without requiring complete personalization of the entire data structure.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing user profile data, search query histories, and usage patterns before generating customized autocomplete suggestions. This advance preparation enables rapid generation of personalized updates when needed, reducing the complexity of real-time data processing.
2Measurement precision
If customized autocomplete data is generated for each user, then the relevance to individual users is improved, but the time and computational resources required are increased
Solution Approach 1:
The patent implements periodic action by generating and distributing customized autocomplete cache updates at scheduled intervals or triggered by specific events (such as accumulating a threshold number of user interactions). This periodic generation reduces the frequency of full data processing cycles while maintaining up-to-date personalization.
Solution Approach 2:
The system applies partial action by generating autocomplete updates based on a selected subset of user profile data and search queries rather than processing all available data. This selective approach maintains high accuracy by focusing on the most relevant data while reducing overall processing time and resource requirements.
3Adaptability or versatility
If autocomplete cache is updated frequently with user-specific data, then the personalization is improved, but the network bandwidth and data transmission are increased
Solution Approach 1:
The patent extracts only the essential and most relevant data elements needed for personalization, such as key usage patterns and frequently searched terms, rather than transmitting complete user profiles and all search histories. This extraction reduces the size of customized updates while preserving the personalization effect.
Solution Approach 2:
The system applies local quality by providing different levels of customization to different users based on their specific needs and data availability. Users with extensive profile data receive more personalized updates, while users with less data receive appropriately scaled personalization, optimizing network usage across the user base.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing customized autocomplete suggestions. First profile data is obtained for a first user. Second profile data is obtained for second users that submitted search queries, where the second users are different from the first user. Based on the first profile data and the second profile data, similarity scores are determined. The similarity scores are each indicative of a degree of similarity between the first user and at least one of the second users. A proper subset of the search queries is selected based on the similarity scores, and an update for an autocomplete cache of a computing device associated with the first user is generated using the selected subset of search queries. The update is provided to the computing device associated with the first user.