Contextual People Recommendation System Using Semantic Vector Correlation
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Solution Overview
Problem
Existing automatic people recommendation techniques fail to accurately identify relevant individuals for communications, often relying on basic signals and lacking deep contextual features, leading to irrelevant recommendations in scenarios like email, meetings, and social networking.
Innovation Solution
A system that generates people recommendations by building scored key phrases in personal profiles, using multi-dimensional vector correlation and deep semantic similarity models to extract contextual signals from user-created communications items, providing proactive or reactive recommendations based on context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If basic signals (first letters, frequent contacts) are used for automatic people recommendation, then the system is simple and easy to operate, but the recommendation quality and relevance are insufficient
Solution Approach 1:
The recommendation system is segmented into multiple independent modules: key phrase extraction module, personal profile building module, and recommendation generation module. Each module processes specific aspects of the data independently, making the complex system manageable and maintainable while improving recommendation quality through specialized processing at each stage.
Solution Approach 2:
The system transitions from basic one-dimensional signals (first letters, frequency counts) to multi-dimensional contextual features (key phrases, semantic meanings, communication patterns, timing information). This dimensional expansion enables the system to capture nuanced relationships between users and contexts, significantly improving recommendation relevance without requiring a complete redesign of the system architecture.
2Measurement precision
If deep contextual features are leveraged to improve recommendation relevance, then the accuracy of identifying relevant individuals improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction and processing of contextual features from communication items before the actual recommendation is needed. Key phrases are extracted and personal profiles are built in advance, so that when a recommendation is requested, the system can quickly query and match against pre-processed data rather than analyzing raw communication items in real-time, significantly reducing processing time.
Solution Approach 2:
The system extracts only the most relevant contextual features (key phrases) from the communication items and stores them in personal profiles. By taking out and storing only the essential information rather than processing all raw data, the system reduces computational overhead during recommendation generation while maintaining high relevance detection accuracy.
3Ease of operation
If manual selection of contacts is used, then the user has full control over the recipient list, but the user must rely on memory and may leave out important people or include irrelevant ones
Solution Approach 1:
The system provides self-service by automatically analyzing communication patterns and generating personalized recommendations based on the user's own historical data. The recommendation system serves the user by leveraging their past communication behavior to predict relevant contacts, reducing the cognitive load on the user while maintaining their control over the final selection.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with recommended contacts are tracked and used to refine future recommendations. When users engage with recommended individuals, this feedback loops back into the personal profile building process, continuously improving the accuracy of recommendations and adapting to changing user needs over time.
Data Source
AI summary
Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.