Interest-Based Keyword Weighting for Location Content
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Solution Overview
Problem
Existing content delivery systems struggle to effectively target and provide relevant content to users based on their interests without explicit keywords, especially in location-based contexts, leading to irrelevant advertisements and reduced user engagement.
Innovation Solution
A method and system that determine user interests through historical search queries and location information, using a content management system with engines for query handling, location determination, prominent entity identification, and interest-based keyword weighting to rank and provide relevant content items, such as advertisements, tailored to the user's location and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content delivery systems use traditional keyword-based or location-based content targeting, then content can be delivered to users, but the content relevance to user interests is low leading to reduced user engagement
Solution Approach 1:
The system performs preliminary analysis of user historical search queries to determine user interests before delivering content. By pre-processing and storing user interest information, the system can quickly match relevant content without requiring complex real-time analysis, thus improving content relevance while maintaining system efficiency
Solution Approach 2:
The system uses user historical search behavior as feedback to continuously refine and update user interest profiles. This feedback mechanism allows the system to learn from past user interactions and improve content matching accuracy over time, enhancing both content relevance and user engagement
2Reliability
If the system analyzes historical search queries to determine user interests, then content relevance improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of content matching into separate functional modules: a user interest determination module that analyzes historical search queries, a prominent entity determination module that identifies relevant locations, and a content selection module that matches content based on both user interests and location. This segmentation reduces overall system complexity by making each module independent and manageable
Solution Approach 2:
The system introduces user interest profiles and prominent entity information as intermediary data structures between raw user behavior data and final content selection. These intermediaries simplify the matching process by pre-processing and structuring information in a way that makes content recommendation more straightforward and less computationally intensive
3Reliability
If the system determines prominent entities and categories based on location, then local content targeting improves, but processing time increases
Solution Approach 1:
The system performs preliminary determination of prominent entities and their categories for various locations and stores this information in advance. When a user requests content based on location, the system can quickly retrieve pre-computed prominent entity information rather than performing complex spatial and categorical analysis in real-time, thus reducing processing time while maintaining accurate local content targeting
Solution Approach 2:
The system determines prominent entities and categories specific to each location rather than using a universal classification scheme. By tailoring the entity determination to local characteristics and pre-computing location-specific information, the system achieves high local content accuracy without the need for complex real-time analysis of each query
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, and including a method for providing content. The method comprises receiving a request for content from a user, the request for content being associated with a location and including one or more keywords. The method further comprises determining a prominent entity in proximity to the location and one or more categories associated with the prominent entity. The method further comprises evaluating historical search queries received from the user to determine one or more interests of the user. The method further comprises determining one or more additional keywords based on the evaluating. The method further comprises determining one or more content items based at least in part on the one or more keywords, the categories and the one or more additional keywords. The method further comprises providing the one or more content items responsive to the request.