Content Item Selection Based on User Interaction Probability
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Solution Overview
Problem
Existing online mapping systems often display irrelevant advertisements to users, degrading the user experience as they are based solely on the location of advertising entities rather than user interests, which are not considered in the content selection process.
Innovation Solution
A method to select content items, such as advertisements, based on the probability of relevance to users by analyzing input data from user interactions, such as search queries and feature selections, to generate content targeting data that identifies relevant topics and their probabilities, allowing for the presentation of ads that are likely to interest users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content items are selected based solely on the address of the advertising entity, then the advertisement selection process is simple, but the relevance to user interests deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user interaction data (search queries, map explorations, feature selections) before selecting advertisements. This pre-analysis creates a user profile that enables more relevant ad selection without adding complexity to the actual advertisement display process.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with the map service and using this information to refine advertisement selection. User behaviors such as searching for specific locations, exploring certain areas, or interacting with map features provide feedback that improves the relevance of subsequent advertisement presentations.
2Reliability
If user interaction data is collected and analyzed to determine content relevance, then the relevance of advertisements to user interests is improved, but the system complexity increases
Solution Approach 1:
The system applies multi-functionality by using the same user interaction data collection infrastructure for multiple purposes: improving advertisement relevance, enhancing map service personalization, and refining user profiling. This universal approach to data utilization amortizes the complexity across multiple benefits rather than creating separate systems for each function.
Solution Approach 2:
The system implements self-service by automatically collecting, analyzing, and applying user interaction data without requiring explicit user input or configuration. Users simply interact with the map service as normal, and the system autonomously uses these interactions to improve advertisement relevance, eliminating the need for users to manually configure their preferences.
3Ease of manufacture
If advertisements are presented based on map location only, then the implementation is straightforward, but the user experience deteriorates due to irrelevant content
Solution Approach 1:
The system performs preliminary analysis of user interactions and map viewing patterns before selecting advertisements. This pre-processing of user behavior data enables the system to present relevant advertisements without requiring complex real-time decision-making during the advertisement selection moment, maintaining implementation simplicity while improving user experience.
Solution Approach 2:
The system introduces an intermediary layer of user profile data that mediates between the simple map location information and the advertisement selection process. This intermediary user profile, built from accumulated interaction data, translates basic location context into personalized advertisement recommendations, bridging the gap between simple implementation and enhanced user experience.
Data Source
AI summary
A content item, e.g., an icon or advertisement content, is selected for placement in a display environment (e.g., on a map or adjacent to a map) in response to a request for the display environment based on a probability that the content item is relevant to a user that is requesting the display environment. The selection is facilitated by content targeting data (e.g., feature selection and query submission) that can be received from user devices while the map space is presented.