Hybrid Information Query System for Personalized Recommendations
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Solution Overview
Problem
Traditional search engines are limited in providing personalized content recommendations as they primarily rely on explicit user data and do not effectively utilize implicit relationships or dynamic user information, leading to less effective recommendations for new or inactive users.
Innovation Solution
A hybrid information query system that collects and analyzes both explicit and implicit user data, incorporating dynamic private information to provide personalized recommendations by establishing a hybrid model that maps user inputs to relevant content, users, or features based on explicit and implicit relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines use only explicit user data for recommendations, then the system complexity is low, but the recommendation effectiveness deteriorates for new or inactive users
Solution Approach 1:
The patent merges explicit user data (traditional search queries and interactions) with implicit user data (behavioral patterns, contextual information, and indirect signals) to create a hybrid recommendation model. This combination allows the system to provide effective recommendations for new or inactive users by leveraging implicit patterns while maintaining the simplicity of explicit data processing for established users.
Solution Approach 2:
The system dynamically adapts its data processing approach based on user characteristics. For new or inactive users with limited explicit data, the system automatically increases reliance on implicit data patterns. For active users with rich explicit interaction history, the system prioritizes explicit data. This dynamic adjustment optimizes recommendation effectiveness without requiring manual system reconfiguration.
2Adaptability or versatility
If the system collects and analyzes both explicit and implicit user data, then the recommendation personalization improves, but the data processing complexity increases
Solution Approach 1:
The patent segments user data into distinct categories: explicit data (direct user inputs, search queries, intentional interactions) and implicit data (behavioral patterns, contextual signals, indirect observations). Each segment is processed through specialized algorithms optimized for its characteristics. This segmentation enables the system to handle diverse data types efficiently while maintaining high personalization capabilities.
Solution Approach 2:
The system introduces intermediary processing layers that bridge explicit and implicit data. These intermediaries include profile construction modules that synthesize multiple data sources into unified user representations, and mapping mechanisms that align implicit behavioral patterns with explicit user preferences. The intermediaries simplify the integration process and reduce overall system complexity.
3Quantity of substance
If traditional recommendation systems only acquire data from direct user interactions, then the data collection process is simple, but the availability of user data is insufficient for new or inactive users
Solution Approach 1:
The system performs preliminary data collection and processing activities before formal user engagement. It pre-processes publicly available information, contextual data, and environmental signals to create initial user profiles. This preliminary action ensures that even new or inactive users have sufficient data for personalized recommendations from their first interaction.
Solution Approach 2:
The system enables users to contribute to their own data profiles through passive observation of their digital footprints and interaction patterns. Users implicitly provide data through their natural online behavior without active participation in data collection processes. This self-service approach continuously enriches user data availability while minimizing the burden on users.
Data Source
AI summary
Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.