Cross-Platform Ad Targeting Using Recipient-Aware Profiles
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Solution Overview
Problem
Existing methods for presenting targeted content on web portals fail to consider the preferences of the gift recipient and do not effectively aggregate information across multiple social networking platforms, leading to irrelevant recommendations.
Innovation Solution
A computer-implemented system that generates identifiers from user and friend attributes, aggregates data across platforms, and uses neural networks and classifiers to determine purchase funnels and insert targeted advertisements into conversational flows, enhancing relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering is used to group users and provide recommendations, then targeted content can be presented to users, but unrelated recommendations are presented when the user is purchasing gifts for others
Solution Approach 1:
The patent introduces an intermediary mechanism that separates the purchaser's identity from the recipient's identity in the recommendation system. By detecting gift-purchasing context and switching to recipient-profile-based recommendations, the system mediates between the user's actual identity and the intended recipient's preferences, resolving the contradiction between personalized recommendations and gift-giving context.
2Adaptability or versatility
If information is gathered from only one social networking platform, then the system is simple to implement, but the targeted content lacks relevance when users socialize on multiple platforms
Solution Approach 1:
The patent implements a universal identifier system that works across multiple social networking platforms. By using platform-agnostic identifiers and standardized data structures, the system achieves multi-functionality in aggregating data from Facebook, LinkedIn, Twitter, and other platforms without proportionally increasing complexity, as the same core aggregation logic applies to each platform.
Solution Approach 2:
The system dynamically changes parameters based on the detected social context. When gift-giving context is detected through analysis of social interactions and platform data, the recommendation parameters switch from purchaser-based to recipient-based profiles, allowing the system to adapt to different scenarios without requiring completely separate systems.
3Measurement precision
If user data is aggregated from multiple social networking platforms, then recommendation relevance improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant data elements from social networking platforms rather than aggregating all available data. By focusing on key attributes such as user identifiers, basic profile information, and context-relevant interactions, the system achieves sufficient profile accuracy without the complexity of processing complete platform datasets.
Solution Approach 2:
The system performs preliminary actions by pre-processing and standardizing data from different platforms before aggregation. By establishing standardized data formats and pre-validated user profiles in advance, the system reduces the complexity of real-time data processing and enables more efficient recommendation generation when gift-giving context is detected.
Data Source
AI summary
A computer-implemented system for presenting targeted content is provided. The system comprises a processor configured to obtain user permission and access profile attributes and friend attributes from a social-network interface, generate, for each person, an identifier from attributes including name, date-of-birth, and location, and store, keyed by the identifier, user search and browsing actions and external records ingested via a distributed data pipeline. Further, the processor is configured to store conversational dialogues as questions and answers using sequence-to-sequence algorithms, or as parent nodes and responses as child nodes of a graph formed on an inverted index. Further, the processor is configured to determine a purchase-funnel stage for the identifier using a classifier trained on search keywords, domains, and page categories, select an advertisement ranked based on revenue, user preference, and context, and generate multi-stage advertisement messages corresponding to discovery, consideration, and action.