How We Built an Adaptive Expert Recommendation System Using AI and Neo4j

Recommendation systems have become essential in connecting people with the content, products, or even other experts they need. Our goal was to create a recommendation system that goes beyond static connections by actively adapting to each user’s interests and preferences. In this article, we walk through how we built an adaptive expert recommendation system that connects users to relevant experts based on their publications and interactions, using AI-driven categorization and Neo4j’s graph database.

Overview of the System

The recommendation system identifies and recommends experts based on open-access publications, categorizes those works into relevant topics, and adapts to each user’s interests. This adaptive model ensures that recommendations improve over time as the system learns from each interaction.

Here’s a high-level breakdown of our approach:

1. Data Collection and Expert Identification
2. Categorization of Publications Using AI
3. Creating a Graph Database in Neo4j
4. Recommendation Engine Based on User Interactions

Each stage incrementally builds an adaptive system that learns and improves with use, providing users with increasingly relevant recommendations.

1. Data Collection and Expert Identification

We started by gathering data from open-access repositories, focusing on reputable sources of research papers and articles to build a comprehensive dataset of experts and their publications. Each publication provided metadata, such as the author(s), title, and research area, which we used to tag experts to specific topics. Many experts had publications across different fields, allowing us to map expertise across multiple subtopics.

2. Categorizing Publications with AI

With our data in place, we turned to natural language processing (NLP) models to categorize each publication by topic. Given the volume and diversity of publications, NLP streamlined the categorization process by:

• Extracting Key Terms and Topics: We used NLP models to analyze abstracts, keywords, and full texts, tagging each publication with relevant topics.
• Supporting Multi-Label Classification: One publication can span multiple subtopics, so we enabled multi-label categorization, allowing each publication to appear across related categories.

This AI-driven approach provided the system with a nuanced view of each expert’s expertise across subtopics, forming a solid foundation for accurate recommendations.

3. Building a Graph Database in Neo4j

With publications categorized, we needed a structure to manage and query complex relationships—Neo4j’s graph database was perfect for the job. Here’s how we structured the graph:

• Nodes and Relationships: Experts, topics, and publications were modeled as nodes, while relationships connected experts to their publications and relevant topics. We created additional relationships between experts who shared similar subtopics, allowing the system to recommend based on connected expertise.
• Connected Experts: Using Neo4j’s Cypher query language, we could identify the most connected experts within a given field. This queryable graph allowed us to find experts who were most relevant to a user’s interests in real-time.

Neo4j’s graph capabilities enabled us to build a system that’s highly efficient at finding connections, forming the backbone of our recommendation engine.

4. Adaptive Recommendations Based on User Interactions

To make the system adaptive, we tracked each user’s interactions with the recommendations and adjusted them based on their preferences. Here’s how the recommendation engine worked:

• Collecting Interaction Data: We tracked user actions, such as which profiles they viewed, the topics they explored, and who they connected with.
• Dynamic Graph Adjustments: Each interaction was feedback for the graph database. If a user frequently explored certain experts or topics, those connections gained more weight in the graph, increasing the relevance of similar recommendations.
• Learning and Adapting: As the system learned from users, it adapted to provide increasingly personalized suggestions over time. If a user’s focus changed, so did the recommendations.

This adaptive mechanism allowed the system to continuously fine-tune recommendations based on real-time insights, creating a personalized and responsive user experience.

Benefits of Using AI and Neo4j for Adaptive Expert Recommendations

By combining AI’s categorization power with Neo4j’s flexible graph structure, we created a system with several key benefits:

• Scalability: The system scales effectively with new experts and publications, enabling future growth without performance trade-offs.
• Real-Time Recommendations: Neo4j’s structure allows us to make real-time queries, ensuring relevant recommendations with minimal latency.
• Adaptability: Through user interaction data, the system adapts continuously, aligning recommendations with user interests over time.

Final Thoughts

Our adaptive expert recommendation system shows how AI and graph databases can be used to create a dynamic, evolving recommendation experience. By categorizing publications and using a flexible graph database, we built a system that not only connects users to relevant experts but also evolves to meet their changing interests.