News Query Expansion via Entity Graphs for Relevance
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Solution Overview
Problem
Consumers face inefficiencies in identifying relevant news articles among the vast array of news content, as existing news organizations and aggregators struggle to accurately determine user interests based on explicit and implicit preferences.
Innovation Solution
A news engine that identifies interest entities from user data, expands queries by incorporating related and category entities, and generates improved news results by combining these expanded queries, organized categorically for presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If news organizations generate and make available news articles covering a broad spectrum of topics, then the quantity and diversity of news content is improved, but the difficulty for consumers to identify interesting articles increases
Solution Approach 1:
The system uses feedback loops where user interactions (clicks, reads, shares) are continuously monitored and fed back into the machine learning models to refine interest entity identification and improve news article recommendations over time
Solution Approach 2:
The system automatically identifies user interests and generates personalized news recommendations without requiring manual user input, using machine learning to self-adjust and improve recommendation accuracy based on observed user behavior patterns
2Measurement precision
If news aggregators identify news articles based on consumer preferences, then the relevance of news to consumers is improved, but the accuracy of identifying relevant content deteriorates due to difficulty in determining user interests
Solution Approach 1:
The system performs preliminary analysis of user data (browsing history, social media activity, demographic information) to pre-identify interest entities before news recommendation, creating a foundation for more accurate matching
Solution Approach 2:
The system expands from traditional single-dimension keyword matching to multi-dimensional entity analysis, incorporating entity relationships, categories, and hierarchical structures to capture user interests more comprehensively
3Adaptability or versatility
If the news engine expands queries by incorporating related and category entities, then the personalization of news results is improved, but the complexity of the query processing system increases
Solution Approach 1:
The query expansion process is segmented into distinct modules: interest entity identification, related entity retrieval, category entity association, and query generation, allowing each component to be optimized independently
Solution Approach 2:
The system introduces intermediary data structures (entity graphs, interest profiles, category hierarchies) that mediate between user data and news articles, simplifying the overall processing by breaking down complex relationships into manageable intermediate representations
Data Source
AI summary
Systems and methods for providing improved news results to a news query according to entity expansion are presented. In response to receiving a news query from a computer user, a news engine identifies one or more interest entities of the computer user. Expanded entity data corresponding to the one or more interest entities is obtained, the expanded entity data identifying related entities to the one or more interest entities. The expanded entity data also includes category data corresponding to the categories of the one or more interest entities. Expanded news queries are generated according to the interest entities, the related entities, and category entities corresponding to the categories. News results are obtained according to the expanded news queries and a news presentation is generated and returned to the computer user.