Personalized Search Result Ordering via User History
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Solution Overview
Problem
Users face overwhelming results from search engines due to the increasing number of documents matching their queries, with little relevance to their specific interests, as existing systems fail to effectively utilize user history and preferences to enhance search and browsing experiences.
Innovation Solution
A method that processes user data by collecting and analyzing user activities, such as search queries, click-throughs, and browsing habits, to create personalized preferences and modify search results, allowing users to revisit and integrate their past activities into their search and browsing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines index and return all matching documents, then the completeness of search results is improved, but the user is overwhelmed by information overload and loses time filtering irrelevant results
Solution Approach 1:
The system performs preliminary actions by collecting and storing user activity data (search queries, clicked results, browsing history) before actual search execution. This historical data is pre-processed and stored in user profiles, enabling rapid personalization of search results without requiring real-time analysis during the search process itself.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with search results (clicks, views, time spent) and using this feedback to refine and personalize future search results. The user profile is dynamically updated based on observed behavior, creating a closed-loop system that progressively improves result relevance.
2Measurement precision
If the system collects and processes extensive user activity data, then the personalization accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments user activity data into distinct categories and event types (searches, clicks, browsing, purchases) with specific data types for each. This segmentation allows the system to process and analyze different kinds of user behavior separately, managing complexity through structured organization of diverse data streams.
Solution Approach 2:
The patent creates a universal data structure and event processing framework that handles multiple types of user activities through a common architecture. The same subscription-based event processing mechanism handles searches, clicks, browsing, and other activities uniformly, reducing system complexity through multi-functional design.
3Reliability
If the system creates detailed user profiles from activity data, then the relevance of search results to user interests is improved, but the processing time and computational resources increase
Solution Approach 1:
User profiles and preference data are built and maintained in advance through continuous background processing of user activities. This preliminary action ensures that when a search is executed, the personalization data is already prepared and stored, allowing rapid retrieval and application without delaying the actual search process.
Solution Approach 2:
The system enables users to subscribe to and manage their own data types and privacy preferences through self-service mechanisms. Users can control what data is collected and how it's used, reducing the need for complex administrative processing while maintaining personalized service.
Data Source
AI summary
A user's prior searching and browsing activities are recorded for subsequent use. A user may examine the user's prior searching and browsing activities in a number of different ways, including indications of the user's prior activities related to advertisements. A set of search results may be modified in accordance with the user's historical activities. The user's activities may be examined to identify a set of preferred locations. The user's set of activities may be shared with one or more other users. The set of preferred locations presented to the user may be enhanced to include the preferred locations of one or more other users. A user's browsing activities may be monitored from one or more different client devices or client application. A user's browsing volume may be graphically displayed.